TT-Assig #2 – see attached

TT-Assig #2 – see attached
TT Assignment #2 Required Readings and Links: Please read Chapter 1 & 2 of of The Future of Translation Technology (See attached) Book: Sin-wai, C. (2019). The Future of Translation Technology. New York: Routledge. Translation Technology PP (See attached) Links (See attached) Assignment: Based on the reading materials for this week, you will write a 1000 word essay responding to the questions mentioned-below. Define, in your opinion, the most significant technological advancement in translation technology history. Please use examples. Please define and list examples to the various translation procedure models. How does using CAT tools aid productivity as a translator and as a translation project manager? What is controllability in translation and what are the advantages and disadvantages? Student participation includes the following attributes: Comments show evidence of a thorough reading and analysis of the material(s) — this means the inclusion of references Submissions utilize correct word usage, spelling, and punctuation. All non-original work MUST be cited, in text and at the end of the document, in APA format, including both published and unpublished sources. To avoid plagiarism, other people’s words MUST be quoted and cited, and other people’s ideas must be cited. For an overview avoiding plagiarism, visit the Purdue University Online Writing Lab: https://owl.english.purdue.edu/owl/resource/589/01/. INSTRUCTIONS TO THE WRITER: Please read the required reading(s). See attached material(s) and links. Follow the instructions and response all the questions. The essay should be no less than 1000 words in length. Sources cited should be from reading documents. Thank you, Customer
TT-Assig #2 – see attached
Google Translate adds 24 new languages, including its first indigenous languages of the Americas https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ Translation as a way to save Indigenous languages https://www.circuitmagazine.org/dossiers-139/translation-as-a-way-to-save-indigenous-languages Google Explains Why App Can’t Translate Most Native American Languages https://www.voanews.com/a/usa_google-explains-why-app-cant-translate-most-native-american-languages/6204275.html
TT-Assig #2 – see attached
6/26/22, 5:12 PM Lingthusiasm – Episode 67: What it means for a language to be… https://lingthusiasm.com/post/682191350734667776/episode-67-what-it-means-for-a-language-to-be 1/4 Epis o d e 6 7: W ha t it m ea ns f or a la ng ua g e t o b e o The R ose tta S to n e is f a m ou s a s a n in sc rip tio n t h a t le t u s r e a d E gyp tia n h ie ro g ly p hs a g ain , b ut it w as c re a te d in t h e p la ce a s p art o f a lo n g h is to ry o f s ig na g e a s p erfo rm ativ e m ult ilin g ua lis m in p ub lic p la ce s. C hoosin g b etw een la ng ua g es is b oth v e ry p ers o n a l b ut it ’s n ot o n ly p ers o n a l – it ’s a ls o a r e s o cie tie s w e liv e in c o n str a in o u r ch oic e s.   In t h is e p is o d e, y o u r h osts La ure n G aw ne a nd G re tc h en M cC ullo ch g et e n th usia stic a b ou t la ng ua g e p olic y a nd h ow o rg aniza tio n s a nd n a tio n -s ta te s m ake la ng ua g e d ecis io n s t h a t a e xce lle n t re ce n t lin g co m m b ook M em ory S p ea ks b y J u lie S ed iv y, t h e In te rn a tio n a l D eca d e o f In d ig en ou s L ang ua g es ( c u rre n tly o n g oin g !) , a nd m any w ays o f u n p ack in g t h e c la ssic q uote a b ou t a la ng ua g e b ein g a d ia le ct w it h a n a rm y a nd a n a vy. R ea d t h e t r a nsc rip t h ere ( h ttp s:/ /lin g th usia sm .c o m /p ost/ 6 82 19 17 18 4 0 83886 0 8/tr a nsc rip t-e p is o d e-6 7-w ha t-it -m ea ns-f o r-a ) . An nou nce m en ts : I n th is m on th ’s b on us e p is o d e ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /w ww.p atre o n .c o m /p osts /6 470 4713 ) w e’r e g ettin g e n th usia stic a b ou t w ord g am es a nd p uzzl es w it h N ic o le H ollid ay a nd B en Z im mer o f S p ecta cu la r V ern a cu la r! W e t a lk a b ou t p atro n q uestio n s, in clu d in g lo ts o f W ord le c o n te n t: w ha t B en a nd N ic o le le a rn ed fro m in te rv ie w in g t h e c re a to r o f W ord le , o u r f a vo u rit e W ord le v a ria nts su ch a s IP A W ord le a nd S em antle , a nd c o m parin g o u r W ord le s o lv in g str a te g ie s w it h a d em o g am e o n a ir . J o in u s o n P atre o n t o lis te n t o t h is a nd 6 0 + o th er b on us e p is o d es ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /w ww.p atre o n .c o m /p osts /6 470 4713 ) . Y ou ’l l a ls o g et a cce ss t o t h e Li ng th usia sm D is c o rd s e rv e r w here y o u c a n p la y a nd d is c u ss w ord g am es a nd p uzzle s w it h o th er la ng ua g e n erd s! H ere a re t h e lin ks m en tion ed in t h is e p is o d e: W ik ip ed ia e n tr y f o r R ose tta S to n e ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .w ik ip ed ia .o rg /w ik i/ R ose tta _ Sto n e) W ik ip ed ia e n tr y f o r D em otic ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .w ik ip ed ia .o rg /w ik i/ D em otic _ (E gyp tia n) W ik ip ed ia e n tr y f o r P to le m y V ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .w ik ip ed ia .o rg /w ik i/ P to le m y_ V _E pip ha nes) Y arra Ri ve r P ro te ctio n ( W ilip -g in B ir ra ru n g m urro n ) A ct 2 0 17 ( h ttp s:/ /w ww.t u m blr .c o m /n eu e_ w eb /if ra m e/e d it / Y arra % 20 Ri ve r% 20 Pro te ctio n % 20 (W ilip – g in % 20 Bir ra ru n g % 20 m urro n )% 20 A ct% 20 20 17 ) A ustr a lia ’s ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /w ww.j u d ic ia lc o lle g e.v ic .e d u.a u/n ew s/a ustr a lia s- rs t-t r il in g ua l- s ta tu te ) M em ory S p ea ks – O n Lo sin g a nd R ecla im in g L ang ua g e a nd S elf b y J u li e S ed iv y ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /w ww.h up .h a rv a rd .e d u/c a ta lo g .p hp ?is b n= 978 0 674 9 80 280 ) Sta n C are y’s r e vie w o f M em ory S p ea ks ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /s ta nca re y.w ord pre ss.c o m /2 0 21/ 0 9/0 7/b ook-r e vie w -m em ory – sp ea ks-o n -lo sin g -a nd -r e cla im in g -la ng ua g e-a nd -s e lf -b y-ju lie -s e d iv y/) W ik ip ed ia e n tr y f o r t h e B en g ali L ang ua g e M ove m en t ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .w ik ip ed ia .o rg /w ik i/ H is to ry _ of_ B ang la d esh # B en g ali_ L ang ua g e_ M ove m en t) W ik ip ed ia e n tr y f o r In te rn a tio n a l M oth er L ang ua g e D ay ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .w ik ip ed ia .o rg /w ik i/ In te rn a tio n a l_ M oth er_ L ang ua g e_ D ay) O ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /w ww.u n .o rg /e n /o b se rv a nce s/m oth er- la ng ua g e-d ay) U nit e d N atio n s D eca d e o f In d ig en ou s L ang ua g es ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /e n .u n esc o .o rg /id il2 0 22-2 0 32 ) In te rn a tio n a l D eca d e o f In d ig en ou s L ang ua g es T w it te r a cco u n t ( h ttp s:/ /tw it te r.c o m /ild eca d e) Lin gth usia sm 6 7: W hat it m ea n s f o r a la n gu ag e t o b e o ff ic ia l Privacy policy 6/26/22, 5:12 PM Lingthusiasm – Episode 67: What it means for a language to be… https://lingthusiasm.com/post/682191350734667776/episode-67-what-it-means-for-a-language-to-be 2/4 You c a n lis te n t o t h is e p is o d e v ia Li ng th usia sm ( h ttp :/ /lin g th usia sm .c o m /) .c o m , S ou n d clo u d ( h ttp s:/ /t.u m blr .c o m /re d ir e ct? z= http % 3A % 2F % 2F so u n d clo u d .c o m %2F lin g th usia sm &t= M Tk4 O TZ kO GFm M mFhN GQ 1M DFiY 2U wN zY yN DN mM mZhM GFm N jJ h N j 67-w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e& m =0& ts = 16 56 2778 13 ) , RS S ( h ttp s:/ /h re f.l i/ ? h ttp :/ /fe ed s.s o u n d clo u d .c o m /u se rs /s o u n d clo u d :u se rs :2 370 550 46 /s o u n d s.r s s) , A pple P od ca sts /iT u n es ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /p od ca sts .a p ple .c o m /u s/p od ca st/ lin g th usia sm /id 118 6 0 56 13 7) , S p otif y ( h ttp s:/ /t.u m blr .c o m /re d ir e ct? z= http s% 3A % 2F % 2Fo p en .s p otif y .c o m %2F sh ow % 2F 4If W Lw qU Ro17 7w 2i4 E cj7 t% 3F si% 3D klE IA _tjRf KyW ZW HcrJT b A & t= N jM zM W RhN 67-w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e& m =0& ts = 16 56 2778 13 ) , Y ou Tu b e ( h ttp s:/ /h re f.l i/ ? h ttp :/ /y o u tu b e.c o m /lin g th usia sm ) , o r w here ve r y o u g et y o u r p od ca sts . Y ou c a n a ls o d ow nlo a d a n m p3 v ia t h e S ou n d clo u d p ag e ( h ttp s:/ /t.u m blr .c o m /re d ir e ct? z= http % 3A % 2F % 2F so u n d clo u d .c o m %2F lin g th usia sm &t= M Tk4 O TZ kO GFm M mFhN GQ 1M DFiY 2U wN zY yN DN mM mZhM GFm N jJ h N j 67-w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e& m =0& ts = 16 56 2778 13 ) f o r o e p is o d e d ro p s, s ig n u p f o r t h e Li ng th usia sm m ailin g lis t ( h ttp s:/ /h re f.l i/ ? h ttp :/ /lin g th usia sm .s u b sta ck .c o m ) . You c a n h elp k e ep Li ng th usia sm a d ve rtis in g -f re e b y s u p portin g o u r P atre o n ( h ttp s:/ /h re f.l i/ ? h ttp :/ /p atre o n .c o m /lin g th usia sm ) . Bein g a p atro n g iv e s y o u a cce ss t o b on us c o n te n t, o u r D is c o rd s e rv e r, a nd o th er p erk s. Li ng th usia sm is o n Fa ce b ook ( h ttp s:/ /t.u m blr .c o m /re d ir e ct? z= http % 3A % 2F % 2F fa ce b ook.c o m %2F lin g th usia sm &t= M GVkY zU 4Y m Q zY 2V iM TE yO TN jM W YyZ W Fm ZD U0N 2Jm N TJm ZD k2 N 2Z jZ 6 7-w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e& m =0& ts = 16 56 2778 13 ) , T u m blr ( h ttp s:/ /lin g th usia sm .t u m blr .c o m ) , I n sta g ra m (h ttp s:/ /h re f.l i/ ? h ttp :/ /in sta g ra m .c o m /lin g th usia sm ) , P in te re st ( h ttp s:/ /h re f.l i/ ? h ttp :/ /p in te re st.c o m /lin g th usia sm ) , a nd Tw it te r (h ttp :/ /tw it te r.c o m /lin g th usia sm ) . Em ail u s a t c o n ta ct [ a t] lin g th usia sm [ d ot] c o m G re tc h en is o n T w it te r a s @ G re tc h en A M cC ( h ttp :/ /tw it te r.c o m /G re tc h en A M cC ) a nd b lo g s a t A ll T hin g s Li ng uis tic ( h ttp s:/ /h re f.l i/ ? h ttp :/ /a llt h in g slin g uis tic .c o m ) . La ure n is o n T w it te r a s @ s u p erlin g uo ( h ttp :/ /tw it te r.c o m /s u p erlin g uo) a nd b lo g s a t S up erlin g uo ( h ttp s:/ /h re f.l i/ ? h ttp :/ /s u p erlin g uo.c o m ) . Li ng th usia sm is c re a te d b y G re tc h en M cC ullo ch a nd L aure n G aw ne. O ur s e n io r p ro d uce r is C la ir e G aw ne ( h ttp s:/ /h re f.l i/ ? h ttp :/ /c la ir e g aw ne.c o m ) , o u r p ro d uctio n e d it o r is S a ra h D op ie ra la ( h ttp s:/ /tw it te r.c o m /S D op ie ra la ) , o u r p ro d uctio n a ssis ta nt is M arth a T su ts u i B illin s ( h ttp s:/ /tw it te r.c o m /m sa to ko ts u b i? la ng = en -G B) , a nd p ro d uctio n m ana g er is Li z M cC ullo u g h (h ttp s:/ /t.u m blr .c o m /re d ir e ct? z= http s% 3A % 2F % 2F w ww.l in ke d in .c o m %2F in % 2Fea m ccu llo u g h% 2F & t= N GFhZ TE 5Y W ZjM GM 5M mE3Y m E0Z W RlOTV kM zF hZ jQ 0N j 67-w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e& m =0& ts = 16 56 2778 13 ) . O ur m usic is ‘A ncie n t C it y ’ b y T he T ria ng le s. ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /m usic .a p ple .c o m /u s/a rtis t/ th e-t r ia ng le s/2 17 79 2 538) T his e p is o d e o f Li ng th usia sm is m ad e a va ila b le u n d er a C re a tiv e C om mon s A ttr ib utio n N on -C om merc ia l S ha re A lik e lic e n se (C C 4 .0 B Y -N C-S A ( h ttp s:/ /h re f.l i/ ? h ttp s:/ /c re a tiv e co m mon s.o rg /lic e n se s/b y-n c-s a /4 .0 /) ) . 2 m on th s a g o ( h ttp s:/ /lin g th usia sm .c o m /p ost/ 68 219 13 50 73 4 667 776/ ep is o d e-67 -w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e) / 8 7 n ote s (h ttp s:/ /lin g th usia sm .c o m /p ost/ 68 219 13 50 73 4 667 776/ ep is o d e-67 -w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e) / S ou rc e S ou n d C lo u d / L in g th usia sm (h ttp s:/ /s o u n d clo u d .c o m /lin g th usia sm /67 -w ha t-it -m ea ns-f o r-a -la ng ua g e-t o -b e-o cia l) # ep is o d es ( http s:/ /lin g th usia sm .c o m /ta g ged /e p is o d es) # ep is o d e 6 7 (http s:/ /lin g th usia sm .c o m /ta g ged /e p is o d e% 20 67) # lin g uis tic s ( http s:/ /lin g th usia sm .c o m /ta g ged /lin g uis tic s) # la ng ua g e ( http s:/ /lin g th usia sm .c o m /ta g ged /la ng ua g e) # la ng ua g e p olic y ( http s:/ /lin g th usia sm .c o m /ta g ged /la ng ua g e% 20 polic y ) # o ( http s:/ /lin g th usia sm .c o m /ta g ged /o cia l% 20 la ng ua g e) # o ( http s:/ /lin g th usia sm .c o m /ta g ged /o cia l% 20 la ng ua g es) # p olic y ( http s:/ /lin g th usia sm .c o m /ta g ged /p olic y ) # p olit ic s ( http s:/ /lin g th usia sm .c o m /ta g ged /p olit ic s) # h is to ry ( http s:/ /lin g th usia sm .c o m /ta g ged /h is to ry ) ( h ttp s:/ /ro ttin g dea d nig htw arrio r.t u m blr .c o m /) ro ttin g dea d nig htw arrio r (h ttp s:/ /ro ttin g dea d nig htw arrio r.t u m blr .c o m /p ost/ 6 874 82 378 2 775 0 2976 ) r e b lo g ged t h is f ro m lin g th usia sm ( h ttp s:/ /lin g th usia sm .c o m /) ( h ttp s:/ /ro ttin g dea d nig htw arrio r.t u m blr .c o m /) ro ttin g dea d nig htw arrio r ( h ttp s:/ /ro ttin g dea d nig htw arrio r.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /q ua lit is t-m usic .t u m blr .c o m /) q ua lit is t-m usic ( h ttp s:/ /q ua lit is t-m usic .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /fa ir y ta le sa nd im ag in in g s.t u m blr .c o m /) fa ir y ta le sa nd im ag in in g s ( h ttp s:/ /fa ir y ta le sa nd im ag in in g s.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /d ais y a ch a in .t u m blr .c o m /) d ais y a ch a in ( h ttp s:/ /d ais y a ch a in .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /la m ala m am .t u m blr .c o m /) la m ala m am ( h ttp s:/ /la m ala m am .t u m blr .c o m /p ost/ 6 834 59 0 18 57713 3 56 8) r e b lo g ged t h is f ro m a llt h in g slin g uis tic ( h ttp s:/ /a llt h in g slin g uis tic .c o m /) (h ttp s:/ /w ww.  6/26/22, 5:12 PM Lingthusiasm – Episode 67: What it means for a language to be… https://lingthusiasm.com/post/682191350734667776/episode-67-what-it-means-for-a-language-to-be 3/4 ( h ttp s:/ /s h a ta r-a eth elw yn n.t u m blr .c o m /) s h a ta r-a eth elw yn n ( h ttp s:/ /s h a ta r-a eth elw yn n.t u m blr .c o m /p ost/ 6 830 60 79 0 42 0 4554 2 4) re b lo g ged t h is f ro m lin g th usia sm ( h ttp s:/ /lin g th usia sm .c o m /) ( h ttp s:/ /la ng ua g ed og 17 .t u m blr .c o m /) la ng ua g ed og 17 ( h ttp s:/ /la ng ua g ed og 17 .t u m blr .c o m /p ost/ 6 82 90 774 56 572 0 8832 ) r e b lo g ged t h is f ro m su p erlin g uo ( h ttp s:/ /w ww.s u p erlin g uo.c o m /) ( h ttp s:/ /la ng ua g ed og 17 .t u m blr .c o m /) la ng ua g ed og 17 ( h ttp s:/ /la ng ua g ed og 17 .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /s a tis ed eye s.t u m blr .c o m /) s a tis ( h ttp s:/ /s a tis ed eye s.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /ja ck e d danie ls .t u m blr .c o m /) ja ck e d danie ls ( h ttp s:/ /ja ck e d danie ls .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /d ig it u re .t u m blr .c o m /) d ig it u re ( h ttp s:/ /d ig it u re .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /5 79 5370 69.t u m blr .c o m /) 5 79 5370 69 ( h ttp s:/ /5 79 5370 69.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /fo g gysc ie n ce a nim etr a sh .t u m blr .c o m /) fo g gysc ie n ce a nim etr a sh ( h ttp s:/ /fo g gysc ie n ce a nim etr a sh .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /m els m .t u m blr .c o m /) m els m ( h ttp s:/ /m els m .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /ju sta g ir lw it h b lo g .t u m blr .c o m /) ju sta g ir lw it h b lo g ( h ttp s:/ /ju sta g ir lw it h b lo g .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /c ry p tic a nt.t u m blr .c o m /) c ry p tic a nt ( h ttp s:/ /c ry p tic a nt.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /th eir re fu ta b le p anca ke .t u m blr .c o m /) th eir re fu ta b le p anca ke ( h ttp s:/ /th eir re fu ta b le p anca ke .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /p aste lp la stic h ea rt.t u m blr .c o m /) p aste lp la stic h ea rt ( h ttp s:/ /p aste lp la stic h ea rt.t u m blr .c o m /p ost/ 6 82 4556 10 46 5386 49 6 ) r e b lo g ged th is f ro m su p erlin g uo ( h ttp s:/ /w ww.s u p erlin g uo.c o m /) ( h ttp s:/ /v o ic e le ssa lv e o la rtr ill.t u m blr .c o m /) v o ic e le ssa lv e o la rtr ill ( h ttp s:/ /v o ic e le ssa lv e o la rtr ill.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /fu ck -y e a hb ts .t u m blr .c o m /) fu ck -y e a hb ts ( h ttp s:/ /fu ck -y e a hb ts .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /m on en eki.t u m blr .c o m /) m on en eki ( h ttp s:/ /m on en eki.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /w ww.s u p erlin g uo.c o m /) s u p erlin g uo ( h ttp s:/ /w ww.s u p erlin g uo.c o m /p ost/ 6 82 452 74 0 2433 9 0 46 4) r e b lo g ged t h is f ro m lin g th usia sm (h ttp s:/ /lin g th usia sm .c o m /) ( h ttp s:/ /d ang ero u sy a ko .t u m blr .c o m /) d ang ero u sy a ko ( h ttp s:/ /d ang ero u sy a ko .t u m blr .c o m /p ost/ 6 82 2812 6616 2384 89 6 ) r e b lo g ged t h is f ro m a llt h in g slin g uis tic ( h ttp s:/ /a llt h in g slin g uis tic .c o m /) ( h ttp s:/ /rid w -a li9 5.t u m blr .c o m /) rid w -a li9 5 ( h ttp s:/ /rid w -a li9 5.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /q cb oeif z zz.t u m blr .c o m /) q cb oeif z zz ( h ttp s:/ /q cb oeif z zz.t u m blr .c o m /p ost/ 6 82 248382 380 6382 0 8) r e b lo g ged t h is f ro m a llt h in g slin g uis tic ( h ttp s:/ /a llt h in g slin g uis tic .c o m /) ( h ttp s:/ /o cca sio n a lp hilo so p hic a lt h in ke r.t u m blr .c o m /) o cca sio n a lp hilo so p hic a lt h in ke r ( h ttp s:/ /o cca sio n a lp hilo so p hic a lt h in ke r.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /s a ra hth eco a t.t u m blr .c o m /) s a ra hth eco a t ( h ttp s:/ /s a ra hth eco a t.t u m blr .c o m /p ost/ 6 82 239 18 4 775 380 992 ) r e b lo g ged t h is f ro m in e ( h ttp s:/ /in e ab le -w rit e r.t u m blr .c o m /) ( h ttp s:/ /s a ra hth eco a t.t u m blr .c o m /) s a ra hth eco a t ( h ttp s:/ /s a ra hth eco a t.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /m anniv u .t u m blr .c o m /) m anniv u ( h ttp s:/ /m anniv u .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /tr a ya na .t u m blr .c o m /) tr a ya na ( h ttp s:/ /tr a ya na .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /m ig ht-a s-w ell- h a p pen .t u m blr .c o m /) m ig ht-a s-w ell- h a p pen ( h ttp s:/ /m ig ht-a s-w ell- h a p pen .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /fra nke n fo ssil.t u m blr .c o m /) fra nke n fo ssil ( h ttp s:/ /fra nke n fo ssil.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /q cb oeif z zz.t u m blr .c o m /) q cb oeif z zz ( h ttp s:/ /q cb oeif z zz.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /lit tle ve lv e te en kn ig ht.t u m blr .c o m /) lit tle ve lv e te en kn ig ht ( h ttp s:/ /lit tle ve lv e te en kn ig ht.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /e xe rc is e sin hum ilia tio n .t u m blr .c o m /) e xe rc is e sin hum ilia tio n ( h ttp s:/ /e xe rc is e sin hum ilia tio n .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /c a p ta in d elila hb ard .t u m blr .c o m /) c a p ta in d elila hb ard ( h ttp s:/ /c a p ta in d elila hb ard .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /n otim possib le ju sta b it u n lik e ly .t u m blr .c o m /) n otim possib le ju sta b it u n lik e ly ( h ttp s:/ /n otim possib le ju sta b it u n lik e ly .t u m blr .c o m /p ost/ 6 82 212 374 9 373 2 14 72 ) r e b lo g ged t h is f ro m allt h in g slin g uis tic ( h ttp s:/ /a llt h in g slin g uis tic .c o m /) ( h ttp s:/ /p yro m on .t u m blr .c o m /) p yro m on ( h ttp s:/ /p yro m on .t u m blr .c o m /p ost/ 6 82 2116 0 80 3713 0 240 ) r e b lo g ged t h is f ro m lin g th usia sm (h ttp s:/ /lin g th usia sm .c o m /) ( h ttp s:/ /la m ala m am .t u m blr .c o m /) la m ala m am ( h ttp s:/ /la m ala m am .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /a ng elic tw .t u m blr .c o m /) a ng elic tw ( h ttp s:/ /a ng elic tw .t u m blr .c o m /) lik e d t h is 6/26/22, 5:12 PM Lingthusiasm – Episode 67: What it means for a language to be… https://lingthusiasm.com/post/682191350734667776/episode-67-what-it-means-for-a-language-to-be 4/4  Pre vio u s p ost ( h ttp s:/ /lin g th usia sm .c o m /p ost/ 6 812 34 2 2713 0 449 92 0 ) N ext p ost  ( h ttp s:/ /lin g th usia sm .c o m /p ost/ 6 82 19 17 18 4 0 83886 0 8) ( h ttp s:/ /a ng elic tw .t u m blr .c o m /) a ng elic tw ( h ttp s:/ /a ng elic tw .t u m blr .c o m /p ost/ 6 82 20 56 90 40 8878 0 80 ) r e b lo g ged t h is f ro m lin g th usia sm (h ttp s:/ /lin g th usia sm .c o m /) ( h ttp s:/ /fo llo w th ew illo th ew is p s.t u m blr .c o m /) fo llo w th ew illo th ew is p s ( h ttp s:/ /fo llo w th ew illo th ew is p s.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /c h ern ob yl.t u m blr .c o m /) c h ern ob yl ( h ttp s:/ /c h ern ob yl.t u m blr .c o m /) lik e d t h is ( h ttp s:/ /lib erty -in -d ea th -1 3 .t u m blr .c o m /) lib erty -in -d ea th -1 3 ( h ttp s:/ /lib erty -in -d ea th -1 3 .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /a ntib it c o in .t u m blr .c o m /) a ntib it c o in ( h ttp s:/ /a ntib it c o in .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /th e–d ash a .t u m blr .c o m /) th e–d ash a ( h ttp s:/ /th e–d ash a .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /b lu eca m om illa .t u m blr .c o m /) b lu eca m om illa ( h ttp s:/ /b lu eca m om illa .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /n a zm azh .t u m blr .c o m /) n a zm azh ( h ttp s:/ /n a zm azh .t u m blr .c o m /) lik e d t h is ( h ttp s:/ /lin g th usia sm .c o m /) lin g th usia sm ( h ttp s:/ /lin g th usia sm .c o m /) p oste d t h is S how m ore n ote s A BO UT L IN GTH USIA SM A p od ca st b y G re tc h en M cC ullo ch a nd L aure n G aw ne. A liv e ly , d eep , la ng ua g e-y c o nve rs a tio n w it h r e a l li n g uis ts ! N ew e p is o d es ( fre e!) t h e t h ir d T hurs d ay o f t h e m on th . L ATE ST T W EETS Lin g th usia sm is b ro u g ht t o y o u in p art b y C alu m M cG ee a nd t h e o p en f ro n t r o u n d ed v o w el [ ɶ ] h ttp s:/ /t.c o /kp EC Z RbK V v ( h ttp s:/ /t.c o /kp EC Z RbK V v) … h ttp s:/ /t.c o /z G O t5 iH HzR ( h ttp s:/ /t.c o /z G O t5 iH HzR ) 6.2 2.2 0 22  e xce lle n t m od als c o n te n t http s:/ /t.c o /g R J2 Tm Jm 9B ( h ttp s:/ /t.c o /g R J2 Tm Jm 9B ) 6.2 1.2 0 22  H as L in g th usia sm g otte n y o u e n th usia stic a b ou t lin g uis tic s a nd n ow y o u w is h y o u k n ew m ore p eo p le t o t a lk w it h w ho s … h ttp s:/ /t.c o /kD y6 G LzkW9 (h ttp s:/ /t.c o /kD y6 G LzkW9 ) 6.2 1.2 0 22  © 2 01 6-2 02 1 – L in g th usia sm  (h ttp s:/ /w ww.t w it te r.c o m /lin g th usia sm )  (h ttp s:/ /w ww.f a ce b ook. co m /lin g th usia  ( h ttp s:/ /p in te re st.c o m /lin g th us  (h ttp s:/ /w ww.l in ke d in .c o  (h ttp s:/ /in sta g ra  ( h ttp s:/ /
TT-Assig #2 – see attached
6/24/22, 8:20 PM Translation as a way to save Indigenous languages – Ottiaq https://www .circuitmagazine.org/dossiers-139/translation-as-a-way-to-save-indigenous-languages 1/3 Accueil Dossiers Translation as a way to save Indigenous languages T ranslation as a way to save Indigenous languages Most Indigenous languages in Canada are in various stages of endangerment, while many others are no longer spoken, so one might wonder why translation is even needed when Indigenous people increasingly speak English or French. But it is the very fact of having arrived at this situation that makes the need for translation, both into and out of the majority languages, all the more urgent. By Marguerite Mackenzie and Julie Brittain, Memorial University , Newfoundland and Labrador A small number of Indigenous languages in Canada, among them Cree, Innu and Naskapi in Québec, and Innu in Labrador , remain relatively vital: they are still being learned by children. Translation (from French or English) into these languages is useful – and often necessary – particularly in legal, medical and resource-development contexts. Conversely , translation from Indigenous languages has become an important means of sharing Indigenous culture and knowledge. Regardless of the direction of translation, a significant factor about the above-mentioned languages, which belong to the Algonquian family , is that they differ markedly in terms of word and sentence structure from either English or French. In these Indigenous languages, words can be very long, and verbs in particular can contain a great deal of information. V erbs include reference to the subject, as shown in (1-3) below and, where relevant, also to direct and indirect objects (1b,c). Adjectival or adverbial information may be included (2a,b), as well as reference to the means by which an action comes about (3a,b). Such differences pose particular challenges for the translation process as equivalencies are often not available. Language communities In all Indigenous language communities, older speakers are dying and younger speakers are at best bilingual in a majority language, but increasingly monolingual in English or French. Moreover , many young people who still speak an Indigenous language have a more limited Northern East Cree (Québec) (1a) Akwâchimâu . ‘the wet snow sticks to snowshoes while a person is travelling’ (1b) Mûmâsâu . ‘s/he eats fish’ (1c) Miyâu . ‘s/he gives it to her/him, it (anim)’ (2) miyu – ~ miyw – = ‘well/good’ (2a) Miyumâkun . ‘it smells good’ (2b) Miywâchimâu . ‘s/he speaks well of her/him’ (3a) Pikunâ pit im .(-pit- = ‘perform an action by pulling’) ‘s/he tears a hole in it’ (3b) Pikunâ nim . (-n- = ‘perform an action using one’s hands’) ‘s/he makes a hole in it with her/his hands’ Numéro 139 Été – 2018 6/24/22, 8:20 PM Translation as a way to save Indigenous languages – Ottiaq https://www .circuitmagazine.org/dossiers-139/translation-as-a-way-to-save-indigenous-languages 2/3 vocabulary and may even have a more restricted range of syntactic structures than older speakers. While the process that results in this restriction is not well understood, it is thought to be due in part to a shift toward a sentence structure that is more English or French; for example, âhkushtikuanâu : some younger speakers will say âhkushû ushtikuân ‘her/his head hurts’ instead of incorporating the noun into the verb as âhku shtikua nâu ‘s/he has a headache’. Use of a two- word construction in place of the more complex single word would probably be judged by more fluent older speakers as, if not exactly ungrammatical, lacking in the grammatical sophistication expected of an adult. This is because in Cree, body parts form a class of noun that is generally incorporated into the verb to form a complex word. In the communities where the authors work, the degree of education of speakers is highly variable, as schooling, mandatory only since the 1950s and ‘60s, was mainly in residential schools, where children were forcibly cut of f from their families and language. The impact has been detailed most recently in the report of the Truth and Reconciliation Commission of Canada, especially created with the mandate to inform all Canadians about what happened in Indian Residential Schools, which includes 97 calls to action for the Canadian government and public. T oday, community schools struggle with a high rate of absenteeism and a low rate of high school completion. Highly educated speakers, fluent in one Indigenous language and English or French, are consequently in high demand for work involving those languages, including translation work. Few have formal training in translation, as it is not generally available. Lack of resources Compounding the issue of there being too few bilingual people to meet the demand for translators, the tools of the trade, so to speak, are as yet lacking or , at best, are still works in progress. In the case of all Canadian Indigenous languages, reference materials – dictionaries and grammars –, if they exist, are incomplete, and, to the authors’ knowledge, no thesaurus for any Indigenous language has been created. Existing dictionaries are bilingual and, at most, have one or two sentences per entry to illustrate appropriate usage, whereas a comprehensive dictionary will have multiple examples illustrating the semantic range of a given entry . An example the authors came across recently highlights the need for such resources: a Cree speaker (incorrectly) used chikimû to refer to an occasion when he had been ‘stuck’ on a plane, unable to get of f for some time. However, the Cree word (see 4 below) does not have the same semantic range as one of the English words that can be used to translate it: It can’t be used in the way the speaker meant it, since it refers specifically to the act of attaching things physically , as in ‘sticking’ one thing to another with glue. These are difficult issues, and translators need resources to check the semantic range of the words s/he is seeking. Canada’ s Indigenous languages have gone into decline so rapidly that there has not been enough time or funding to keep up with the demand for resources. Paving the way for translation Specialized translation into Indigenous languages comes with many challenges. Since every field has its own technical vocabulary , specialized glossaries are needed. This involves the creation of many new words/phrases in Indigenous languages extensive work which requires the collaboration of translators, specialists from the field, and often linguists. In Cree, Naskapi or Innu, for example, technical terms often translate best as phrases, as shown in (5). Other common candidates for translation into Indigenous languages include religious works (e.g., the Bible), government publications (e.g., books, pamphlets, posters), and curriculum materials for schools which offer mother tongue education. In Canada, most materials are translations from English or French. These fields may not have a highly technical vocabulary , but they often contain jargon that needs to be translated first into plain language, and then into the Indigenous language. Translation from an Indigenous language into English or French frequently involves an oral performance, which must first be properly transcribed. It usually falls to the translator to do this work, a task requiring its own rigorous methodology , which we detail in Brittain and MacKenzie 2011. The Naskapi of Kawawachikamach, a community 15 kilometres north-east of Shef ferville, Québec, are very keen to see their literature made available in English alongside their own language, and they have provided ongoing funding for a team of older and younger speakers, along with linguists who have studied the language, to carry out transcription, translation and publication of sound recordings from the 1960s. The linguists type in the Roman alphabet and then convert to the syllabic writing system of the community , the older speakers clarify less-known vocabulary and grammatical structures, the younger speakers provide translations in English for discussion, and the literal translations are then re-written in literary English, matching the tone of the original speech. To date, six Naskapi/English story books have been published (Peastitute 2013, 2014, 2015, 2016a,b, 2017). Political considerations The issue of access to Indigenous cultural knowledge by outsiders through translation into majority languages is highly politicized in some communities, and some Indigenous groups have taken a stand against translation being done at all. For example, Carrie Dyck, an Associate Professor in the Department of Linguistics at Memorial University , describes this issue for Cayuga, an Iroquoian language, with respect to longhouse religious teachings, which cannot be accessed by anyone who has not undertaken an apprenticeship to qualify as an orator on behalf of the (4) chikimû ‘s/he, it (anim) is attached, stuck’ (5) Sedative ‘A drug taken for its calming or sleep-inducing effect’ natukun tshetshi nipeshkatshet put kie tshetshi tshinamipit etenimut 6/24/22, 8:20 PM Translation as a way to save Indigenous languages – Ottiaq https://www .circuitmagazine.org/dossiers-139/translation-as-a-way-to-save-indigenous-languages 3/3 community . Not all community members agree with the restriction, as many younger people can now understand the teachings only through translation. Toward the future Even though Canada’ s Indigenous languages are declining at an alarming rate and many have already ceased being spoken, an increasing number of communities are engaged in language recovery , a process in which translation has a crucial role to play . In many communities, younger people are learning their languages as a second language and translation into the Indigenous language creates the materials needed to support this work and promote literacy in the language. The large amount of material needed to promote fluent reading is often far beyond the scope and budget of a community or organization. It has become common for existing children’s literature, for instance, to be translated into Cree and Mi’kmaq, with the permission of the author (e.g., books by Robert Munsch). The best use must be made of the limited resources that exist, chief among these being fluent speakers, but there also needs to be better access to funding. Marguerite Mackenzie and Julie Brittain are both professors at Memorial University, Newfoundland and Labrador’s University . CITED WORKS Brittain, Julie and Marguerite MacKenzie (201 1). “Translating Algonquian Oral T exts.” In Swann 2011, pp. 242-274. Dyck, Carrie. (2011). “Should Translation Work Take Place? Ethical questions concerning the translation of First Nation languages. In Swann 201 1, pp. 17-42. Peastitute, John. (2017) Caught in a Blizzard and other stories told by Naskapi Elder John Peastitute. (Naskapi/English). Translated by Alma Chemaganish & Silas Nabinicaboo, with literary translation by Julie Brittain, edited and annotated by Marguerite MacKenzie. Kawawachikamach, QC: Naskapi Development Corporation. [See also Misti-Michisuw: The Giant Eagle and other stories (2016b), Umayichis: A Naskapi Legend (2016a), Achan: Naskapi Giant Stories (2015), Chahkapas: A Naskapi Legend (2014), Kuihkwahchaw: Naskapi Wolverine Stories (2013) available from lulu.com] Swann, Brian. (2011). Editor. Born in the Blood: On Native American T ranslation , Native Literature of the Americas Series. Lincoln: University of Nebraska Press.
TT-Assig #2 – see attached
6/26/22, 8:18 PM Google Explains Why App Can’t Translate Most Native American Languages https://www.voanews.com/a/usa_google-explains-why-app-cant-translate-most-native-american-languages/6204275.html 1/3 USA Google Explains Why App Can’t Translate Most Native American Languages April 09, 2021 3:03 AM Cecily Hilleary Bill Waawaate is Indigenous, smart, educated, and the millionaire-founder of a highly successful snowmobile company. He also is a comic book superhero from a First Nation in Canada. “The aim here is to help Canadians understand Indigenous culture and to erase the stereotypes about First Nations communities,” said Joseph John, the Montreal-based designed and publisher of the Citizen Canada comic book series. Johns wanted his feather-caped superhero to speak English, French and Cree, a language spoken by more than 95,000 First Nations people in Canada. He assumed he could rely on Google Translate for help. But the app, which supports 109 languages, does not o 150 Indigenous languages spoken today in North America. So Johns started up an online petition urging Google to add Cree to its translation engine. That petition has so far received nearly all the 7,500 signatures he had hoped for. “For me, it just doesn’t make sense,” John told VOA. “Google Translate does o the Indigenous language of New Zealand, which is spoken by only about 50,000 persons. How can a company with 135,000 people working for it in 40 nations across the globe not VOA posed the question to Google. 6/26/22, 8:18 PM Google Explains Why App Can’t Translate Most Native American Languages https://www.voanews.com/a/usa_google-explains-why-app-cant-translate-most-native-american-languages/6204275.html 2/3 “Indigenous languages are incredibly important to us,” Google spokesperson Justin Burr said via email. As it turns out, though, Cree is a “low resource” language, which means there aren’t enough written translations of Cree documents to populate and “train” automated translation systems like Google’s. Burr said Google is actively working toward adding more low resource languages. “One of those ways is we lean heavily on our contributor community , which allows native speakers to add valuable feedback, verify translations, et cetera, to languages that we do support, as well as languages we have yet to support,” said Burr. “Beyond that, we are working on new machine learning techniques that allow us to support the low resource languages with less training data.” University of Colorado linguist Andrew Cowell specializes in Indigenous-language documentation. He explained to VOA some of the challenges for a machine to translate Indigenous languages. “Most of the world’s languages aren’t written. They are spoken as household or community languages that are not regularly used in any kind of literate way,” said Cowell. “The pattern all over the world is that someone speaks one language at home and then they write in the national language. And so that language isn’t represented online. And even if it is, there won’t be any standardized writing system because people make it up as they go.” Adding a language to Google Translate requires the input of “hundreds of millions of words,” according to Cowell. “And it needs to be what’s called ‘clean data,’ which means that you have the same spelling and grammar conventions.” Cree is actually a series of dialects that gradually change across Canada. “Cree is actually considered to be multiple di Wood Cree, Swampy Cree, Plains Cree, et cetera,” said Cowell. “Even within those languages, there is a good deal of regional variation. So, the ‘Cree language’ is more complex — and each community of speakers is smaller — than would be suggested by statements that ‘95,000 people speak Cree.'” Projects in the works 6/26/22, 8:18 PM Google Explains Why App Can’t Translate Most Native American Languages https://www.voanews.com/a/usa_google-explains-why-app-cant-translate-most-native-american-languages/6204275.html 3/3 Google says plans are under way to add Guarani, an Indigenous language spoken in Paraguay, Brazil and Bolivia; plus Inukitut, spoken across the North American Arctic and in Greenland; and Tsalagi, the Cherokee language, which has plenty of translated material. In the early 1800s, a Cherokee named Sequoyah developed a Tsalagi syllabary , a traditional writing system made up of symbols. In 1828, the tribe began publishing the Cherokee Phoenix newspaper. All historic materials, including religious texts, the Cherokee constitution and laws, use Sequoyah’s syllabary, and today, learning materials are still being written in syllabics. The Cherokee Nation’s Language Program department spent nearly two years working with Google to translate more than 50,000 technology terms into Cherokee and developed a syllabary font that Google already has added to its search engine, as well as Gmail, Chromebooks and Android. But adding Tsalagi to Google translate will take more time — and money. “We are just researching the amount of resources and manpower it will require,” said Roy Boney, manager of the Cherokee Nation’s Language Program, in an emailed statement to VOA. “Currently, we are consulting with linguists at the University of New Mexico and University of Mexico City and also exploring grant opportunities in order to expand our research base.” In the meantime, Cherokee linguists are studying and documenting Tsalagi grammar and syntax, with the goal of pairing it up with already translated texts. “This will help us develop the proper Cherokee language data to start training machine translation engines,” Boney said. As for Cree—and many other Indigenous languages—Cowell says speakers will have to wait, adding, “I think there are going to be increasing number of communities starting to write with some kind of standardized orthography, I’m hopeful that additional Indigenous languages will be added to translation engines like Google in the future.”
TT-Assig #2 – see attached
6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 1/9 Goo g le T ra n sla te a d ds 2 4 n ew l a n g uag es, i n clu d in g i t s fir s t i n d ig en o us l a n g uag es o f t h e A m eric a s Sara h P ere z @ sa ra h in ta m pa / 1 :1 7 P M ED T •Ma y 1 1, 2 022 Image Credits: Google In a d dit io n to im pro vin g G oogle A ssis ta nt’s a bilit y to c o m munic a te w it h u se rs in a m ore n atu ra l w ay, G oogle to day a nnounce d im pro ve m ents to it s G oogle T ra nsla te s e rv ic e . T he c o m pany s a id it ’s a ddin g 2 4 n ew la nguages — in clu din g it s fir s t in dig enous la ngu ages o f th e A m eric a s w it h th e a ddit io ns o f Q uech ua, G uara ni a nd A ym ara . O th er a ddit io ns in clu de th e fo llo w in g: In to ta l, th e 2 4 n ew la nguages a re s p oke n b y m ore th an 3 00 m illio n p eople w orld w id e, G oogle s a id . T u rn e q uit y i n to l iq uid it y L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p . 6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 2/9 “T his r a nges fr o m s m alle r la nguages, lik e M iz o s p oke n b y p eople in th e n o rth east o f In dia — b y a bout 8 00,0 00 p eople — u p to v e ry la rg e w orld la nguages lik e L in ga la s p oke n b y a ro und 4 5 m illio n p eop le a cro ss C entr a l A fr ic a ,” s a id Is a ac C asw ell, a G oogle T ra nsla te R ese arc h S cie ntis t. H e a d ded th at in a ddit io n to th e in dig enous la nguages o f th e A m eric a s, G oogle T ra nsla te w ill s u pport a d ia le ct o f E nglis h fo r th e fir s t tim e w it h K rio fr o m S ie rra L eone. T he c o m pany s a id it s e le cte d th is n e w est b atc h o f la nguages to s u pport b y lo okin g fo r la ngu ages w it h v e ry la rg e b ut u nders e rv e d p opula tio ns — w hic h w ere fr e q uently in th e A fr ic a n c o ntin ent a nd In dia n su bco ntin ent. It a ls o w ante d to a ddre ss in dig enous la nguages, w hic h a re o fte n o ve rlo oke d b y te ch nolo gy. T e ll u s a b out y o urs e lf Y o u c o uld w in a n ew A pp le i P ad m in i! T A KE T H E S U RV EY Spo nso re d b y L in q to L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p . 6/26/22, 8:21 PM Google Translate adds 24 new languages, including its first indigenous languages of the Americas | T echCrunch https://techcrunch.com/2022/05/11/google-translate-adds-24-new-languages-including-its-first-indigenous-languages-of-the-americas/ 3/9 More T e ch C ru nch G oogle ’s a bilit y to a dd n ew la nguage s h as im pro ve d th anks to te ch nolo gic a l a dva nce s ta kin g p la ce o ve r th e p ast fe w y e ars , C asw ell s a id . “U p u ntil a c o uple o f y e ars a go, it s im ply w as n ot te ch nolo gic a lly p ossib le to a dd la nguages lik e th ese , w hic h a re w hat w e c a ll a lo w r e so urc e — m eanin g th at th ere a re n ot v e ry m any te xt r e so urc e s o ut th ere fo r th em ,” h e e xp la in ed. B ut a n ew te ch nolo gy ca lle d Z ero -S hot M ach in e T ra nsla tio n h as m ade it e asie r. “ A t a h ig h le ve l, th e w ay y o u c a n im agin e it w ork in g is y o u h ave a s in gle g ig antic n eura l A I m odel, a nd it ’s tr a in ed o n 1 00 d if f e re nt la nguages w it h tr a nsla tio n. Y ou c a n th in k o f it a s a p oly g lo t th at kn ow s lo ts o f la nguages. B ut th en a d dit io nally , it g ets to s e e te xt in 1 ,0 00 m ore la nguages th at is n ’t tr a nsla te d. Y ou c a n im agin e if y o u’r e s o m e b ig p oly g lo t, a nd th en y o u ju st s ta rt r e adin g n ove ls in a noth er la nguage, y o u c a n s ta rt to p ie ce to geth er w hat it c o uld m ean b ase d o n y o ur k n ow le dge o f la nguage in g enera l, ” h e s a id . T he e xp ansio n b rin gs th e to ta l n um ber o f la nguages s u pporte d b y th e s e rv ic e to 1 33. B ut G oogle s a id th e s e rv ic e s till h as a lo ng w ay t o g o, a s th ere a re s till s o m e 7 ,0 00 u nsu pporte d la nguages g lo bally th at T ra nsla te d oesn ’t a ddre ss. T he n ew la nguages w ill b e liv e to day o n G oogle T ra nsla te , b ut w on’t r e ach a ll u se rs w orld w id e fo r a c o uple o f d ays, G oogle n ote d . W elc o m e t o T e ch C ru nch c o m men ts ! P le ase k e ep c o n ve rs a tio n s c o u rte o u s a n d o n -t o p ic . S e e o u r c o m munit y g u id elin es f o r m ore in fo rm atio n . C on ve rs a tio n Lo g in S ig n u p B e t h e rs t t o c o m men t… L o gin S earc h TC S essio ns: R o bo tic s S ta rtu p s Te ch C ru n ch + A ud io N ew sle tte rs S ta rtu p B attle fie ld A dve rtis e E ve nts M ore J oin T e ch C ru n ch + G et a c ce ss t o e xp ert a d vic e o n f u n d ra is in g , g ro w th , a n d m an ag em en t f o r y o ur s ta rtu p .
TT-Assig #2 – see attached
3 1 THE DEVELOPMENT OF TRANSLATION TECHNOLOGY 1967–2013 Chan Sin-wai the chinese university ff hfng kfng, hfng kfng, china Introduction The history of translation technology, or more specifically computer-aided translation, is short, but its development is fast. It is generally recognized that the failure of machine translation in the 1960s led to the emergence of computer-aided translation. The development of computer- aided translation from its beginning in 1967 as a result of the infamous ALPAC report (1966) to 2013, totalling 46 years, can be divided into four periods. The first period, which goes from 1967 to 1983, is a period of germination. The second period, covering the years between 1984 and 1993, is a period of steady growth. The third period, which is from 1993 to 2003, is a decade of rapid growth. The last period, which includes the years from 2004 to 2013, is a period of global development. 1967–1983: A period of germination Computer-aided translation, as mentioned above, came from machine translation, while machine translation resulted from the invention of computers. Machine translation had made considerable progress in a number of countries from the time the first computer, ENIAC, was invented in 1946. Several events before the ALPAC report in 1966 are worth noting. In 1947, one year after the invention of the computer, Warren Weaver, President of the Rockefeller Foundation and Andrew D. Booth of Birkbeck College, London University, were the first two scholars who proposed to make use of the newly invented computer to translate natural languages (Chan 2004: 290−291). In 1949, Warren Weaver wrote a memorandum for peer review outlining the prospects of machine translation, known in history as ‘Weaver’s Memorandum’. In 1952, Yehoshua Bar-Hillel held the first conference on machine translation at the Massachusetts Institute of Technology, and some of the papers were compiled by William N. Locke and Andrew D. Booth into an anthology entitled Machine Translation of Languages: Fourteen Essays, the first book on machine translation (Locke and Booth 1955). In 1954, Leon Dostert of Georgetown University and Peter Sheridan of IBM used the IBM701 machine to make a public demonstration of the translation of Russian sentences into English, which marked a milestone in machine translation (Hutchins 1999: 1−16; Chan 2004: 4 125−226). In the same year, the inaugural issue of Mechanical Translation, the first journal in the field of machine translation, was published by the Massachusetts Institute of Technology (Yngve 2000: 50−51). In 1962, the Association for Computational Linguistics was founded in the United States, and the journal of the association, Computational Linguistics, was also published. It was roughly estimated that by 1965, there were eighteen countries or research institutions engaged in the studies on machine translation, including the United States, former Soviet Union, the United Kingdom, Japan, France, West Germany, Italy, former Czechoslovakia, former Yugoslavia, East Germany, Mexico, Hungary, Canada, Holland, Romania, and Belgium (Zhang 2006: 30−34).The development of machine translation in the United States since the late 1940s, however, fell short of expectations. In 1963, the Georgetown machine translation project was terminated, which signifies the end of the largest machine translation project in the United States (Chan 2004: 303). In 1964, the government of the United States set up the Automatic Language Processing Advisory Committee (ALPAC) comprising seven experts to enquire into the state of machine translation (ALPAC 1966; Warwick 1987: 22–37). In 1966, the report of the Committee, entitled Languages and Machines: Computers in Translation and Linguistics, pointed out that ‘there is no immediate or predictable prospect of useful machine translation’ (ALPAC 1966: 32). As machine translation was twice as expensive as human translation, it was unable to meet people’s expectations, and the Committee recommended that resources to support machine translation be terminated. Its report also mentioned that ‘as it becomes increasingly evident that fully automatic high-quality machine translation was not going to be realized for a long time, interest began to be shown in machine-aided translation’ (ibid.: 25). It added that machine translation should shift to machine-aided translation, which was ‘aimed at improved human translation, with an appropriate use of machine aids’ (ibid.: iii), and that ‘machine-aided translation may be an important avenue toward better, quicker, and cheaper translation’ (ibid.: 32). The ALPAC report dealt a serious blow to machine translation in the United States, which was to remain stagnant for more than a decade, and it also made a negative impact on the research on machine translation in Europe and Russia. But this gave an opportunity to machine-aided translation to come into being. All these events show that the birth of machine- aided translation is closely related to the development of machine translation. Computer-aided translation, nevertheless, would not be possible without the support of related concepts and software. It was no mere coincidence that translation memory, which is one of the major concepts and functions of computer-aided translation, came out during this period. According to W. John Hutchins, the concept of translation memory can be traced to the period from the 1960s to the 1980s (Hutchins 1998: 287−307). In 1978, when Alan Melby of the Translation Research Group of Brigham Young University conducted research on machine translation and developed an interactive translation system, ALPS (Automated Language Processing Systems), he incorporated the idea of translation memory into a tool called ‘Repetitions Processing’, which aimed at finding matched strings (Melby 1978; Melby and Warner 1995: 187). In the following year, Peter Arthern, in his paper on the issue of whether machine translation should be used in a conference organized by the European Commission, proposed the method of ‘translation by text-retrieval’ (Arthern 1979: 93). According to Arthern, This information would have to be stored in such a way that any given portion of text in any of the languages involved can be located immediately … together with its translation into any or all of the other languages which the organization employs. (Arthern 1979: 95) S. Chan Development of translation technology5 In October 1980, Martin Kay published an article, entitled ‘The Proper Place of Men and Machines in Language Translation’, at the Palo Alto Research Center of Xerox. He proposed to create a machine translation system in which the display on the screen is divided into two windows. The text to be translated appears in the upper window and the translation would be composed in the bottom one to allow the translator to edit the translation with the help of simple facilities peculiar to translation, such as aids for word selection and dictionary consultation, which are labelled by Kay as a translator amanuensis (Kay 1980: 9−18). In view of the level of word-processing capacities at that time, his proposal was inspiring to the development of computer-aided translation and exerted a huge impact on its research later on. Kay is generally considered a forerunner in proposing an interactive translation system. It can be seen that the idea of translation memory was established in the late 1970s and the 1980s. Hutchins believed that the first person to propose the concept of translation memory was Arthern. As Melby and Arthern proposed the idea almost at the same time, both could be considered as forerunners. And it should be acknowledged that Arthern, Melby, and Kay made a great contribution to the growth of computer-aided translation in its early days. The first attempt to deploy the idea of translation memory in a machine translation system was made by Alan Melby and his co-researchers at Brigham Young University, who jointly developed the Automated Language Processing Systems, or ALPS for short. This system provided access to previously translated segments which were identical (Hutchins 1998: 291). Some scholars classify this type of full match a function of the first generation translation memory systems (Gotti et al. 2005: 26−30; Kavak 2009; Elita and Gavrila 2006: 24−26). One of the major shortcomings of this generation of computer-aided translation systems is that sentences with full matching were very small in number, minimizing the reusability of translation memory and the role of translation memory database (Wang 2011: 141). Some researchers around 1980 began to collect and store translation samples with the intention of redeploying and sharing their translation resources. Constrained by the limitations of computer hardware (such as its limited storage space), the cost of building a bilingual database was high, and with the immaturity in the algorithms for bilingual data alignment, translation memory technology had been in a stage of exploration. As a result, a truly commercial computer-aided translation system did not emerge during the sixteen years of this period and translation technology failed to make an impact on translation practice and the translation industry. 1984–1992: A period of steady growth The eight years between 1984 and 1992 are a period of steady growth for computer-aided translation and for some developments to take place. Corporate operation began in 1984, system commercialization, in 1988, and regional expansion, in 1992. Company operation It was during this period that the first computer-aided translation companies, Trados in Germany and Star Group in Switzerland, were founded in 1984. These two companies later had a great impact on the development of computer-aided translation. The German company was founded by Jochen Hummel and Iko Knyphausen in Stuttgart, Germany, in 1984. Trados GmbH came from TRAnslation and DOcumentation Software. This company was set up initially as a language service provider (LSP) to work on a translation project they received from IBM in the same year. As the company later developed computer- 6 aided translation software to help complete the project, the establishment of Trados GmbH is regarded as the starting point of the period of steady growth in computer-aided translation (Garcia and Stevenson 2005: 18–31; http://www.lspzone.com).Of equal significance was the founding of the Swiss company STAR AG in the same year. STAR, an acronym of Software, Translation, Artwork, and Recording, provided manual technical editing and translation with information technology and automation. Two years later, STAR opened its first foreign office in Germany in order to serve the increasingly important software localization market and later developed STAR software products, GRIPS and Transit for information management and translation memory respectfully. At the same time, client demand and growing export markets led to the establishment of additional overseas locations in Japan and China. The STAR Group still plays an important role in the translation technology industry (http://www.star-group.net). It can be observed that during this early period of computer-aided translation, all companies in the field were either established or operated in Europe. This Eurocentric phenomenon was going to change in the next period. System commercialization The commercialization of computer-aided translation systems began in 1988, when Eiichiro Sumita and Yutaka Tsutsumi of the Japanese branch of IBM released the ETOC (Easy to Consult) tool, which was actually an upgraded electronic dictionary. Consultation of a traditional electronic dictionary was by individual words. It could not search phrases or sentences with more than two words. ETOC offered a flexible solution. When inputting a sentence to be searched into ETOC, the system would try to extract it from its dictionary. If no matches were found, the system would make a grammatical analysis of the sentence, taking away some substantive words but keeping the form words and adjectives which formed the sentence pattern. The sentence pattern would be compared with bilingual sentences in the dictionary database to find sentences with a similar pattern, which would be displayed for the translator to select. The translator could then copy and paste the sentence onto the Editor and revise the sentence to complete the translation. Though the system did not use the term translation memory and the translation database was still called a ‘dictionary’, it nevertheless had essentially the basic features of translation memory of today. The main shortcoming of this system is that as it needs to make grammatical analyses, its programming would be difficult and its scalability would be limited. If a new language were to be added, a grammatical analysis module would have to be programmed for the language. Furthermore, as the system could only work on perfect matching but not fuzzy matching, it drastically cut down on the reusability of translations (Sumita and Tsutsumi 1988: 2). In 1988, Trados developed TED, a plug-in for text processor tool that was later to become, in expanded form, the first Translator’s Workbench editor, developed by two people and their secretary (Garcia and Stevenson 2005: 18–31). It was around this time that Trados made the decision to split the company, passing the translation services part of the business to INK in the Netherlands, so that they could concentrate on developing translation software (http://www. translationzone.com). Two years later, the company also released the first version of MultiTerm as a memory- resident multilingual terminology management tool for DOS, taking the innovative approach of storing all data in a single, freely structured database with entries classified by user-defined attributes (Eurolux Computers 1992: 8; http://www.translationzone.com; Wassmer 2011). S. Chan Development of translation technology7 In 1991 STAR AG also released worldwide the Transit 1.0 (‘Transit’ was derived from the phrase ‘translate it’) 32-bit DOS version, which had been under development since 1987 and used exclusively for in-house production. Transit featured the modules that are standard features of today’s CAT systems, such as a proprietary translation editor with separate but synchronized windows for source and target language and tag protection, a translation memory engine, a terminology management component and project management features. In the context of system development, the ideas of terminology management and project management began with Transit 1.0. Additional products were later developed for the implementation and automation of corporate product communications: TermStar, WebTerm, GRIPS, MindReader, SPIDER and STAR James (http://www.star-group.net). One of the most important events in this period is obviously the release of the first commercial system, Trados, in 1992, which marks the beginning of commercial computer- aided translation systems. Regional expansion The year 1992 also marks the beginning of the regional expansion of computer-aided translation. This year witnessed some significant advances in translation software made in different countries. First, in Germany, Translator’s Workbench I and Translator’s Workbench II (DOS version of Trados) were launched within the year, with Workbench II being a standalone package with an integrated editor. Translator’s Workbench II comprises the TW II Editor (formally TED) and MultiTerm 2. Translator’s Workbench II was the first system to incorporate a ‘translation memory’ and alignment facilities into its workstation. Also of considerable significance was the creation by Matthias Heyn of Trados’s T Align, later known as WinAlign, the first alignment tool on the market. In addition, Trados began to open  a network of global offices, including Brussels, Virginia, the United Kingdom and Switzerland (Brace 1994; Eurolux Computers 1992; http://www.translationzone.com; Hutchins 1998: 287–307). Second, in the United States, IBM launched its IBM Translation Manager / 2 (TM/2), with an Operating System/2 (OS/2) package that integrated a variety of translation aids within a Presentation Manager interface. TM/2 had its own editor and a translation memory feature which used fuzzy search algorithms to retrieve existing material from its translation database. TM/2 could analyse texts to extract terms. TM/2 came with lemmatizers, spelling lists, and other linguistic resources for nineteen languages, including Catalan, Flemish, Norwegian, Portuguese, Greek, and Icelandic. External dictionaries could also be integrated into TM/2, provided they were formatted in Standard Generalized Markup Language (SGML). TM/2 could be linked to logic-based machine translation (Brace 1992a). This system is perhaps the first hybrid computer-aided translation system that was integrated with a machine translation system (Brace 1993; Wassmer 2011). Third, in Russia, the PROMT Ltd was founded by two doctorates in computational linguistics, Svetlana Sokolova and Alexander Serebryakov, in St. Petersburg in 1991. At the beginning, the company mainly developed machine translation (MT) technology, which has been at the heart of the @promt products. Later, it began to provide a full range of translation solutions: machine translation systems and services, dictionaries, translation memory systems, data mining systems (http://www.promt.com). Fourth, in the United Kingdom, two companies specializing in translation software production were founded. First, Mark Lancaster established the SDL International, which served as a service provider for the globalization of software (http://www.sdl.com). Second, 8 ATA Software Technology Ltd, a London-based software house specializing in Arabic translation software, was established in 1992 by some programmers and Arabic software specialists. The company later developed a series of machine translation products (Arabic and English) and MT and TM hybrid system, Xpro7 and online translation engine (http://www. atasoft.com). 1993–2003: A period of rapid growth This period, covering the years from 1993 to 2003, is a period of rapid growth, due largely to (1) the emergence of more commercial systems; (2) the development of more built-in functions; (3) the dominance of Windows operation systems; (4) the support of more document formats; (5) the support of more languages for translation; and (6) the dominance of Trados as a market leader. (1) The emergence of more commercial systems Before 1993, there were only three systems available on the market, including Translator’s Workbench II of Trados, IBM Translation Manager / 2, and STAR Transit 1.0. During this ten-year period between 1993 and 2003, about twenty systems were developed for sale, including the following better-known systems such as Déjà Vu, Eurolang Optimizer (Brace 1994), Wordfisher, SDLX, ForeignDesk, Trans Suite 2000, Yaxin CAT, Wordfast, Across, OmegaT, MultiTrans, Huajian, Heartsome, and Transwhiz. This means that there was a six- fold increase in commercial computer-aided translation systems during this period. Déjà Vu is the name of a computer-aided translation system developed by Atril in Spain after 1993. A preliminary version of Déjà Vu, a customizable computer-aided translation system that combined translation memory technology with example-based machine translation techniques, was initially developed by ATRIL in June to fulfil their own need for a professional translation tool. At first, they worked with machine translation systems, but the experiments with machine translation were extremely disappointing, and subsequent experiences with translation memory tools exposed two main shortcomings: all systems ran under MS-DOS and were capable of processing only plain text files. Then, ATRIL began considering the idea of writing its own translation memory software. Déjà Vu 1.0 was released to the public in November 1993. It was with an interface for Microsoft Word for Windows 2.0, which was defined as the first of its kind. Version 1.1 followed soon afterwards, incorporating several performance improvements and an integrated alignment tool (at a time when alignment tools were sold as expensive individual products), and setting a new standard for the translation tool market (http://www.atril.com). Déjà Vu, designed to be a professional translation tool, produced acceptable results at an affordable price. Déjà Vu was a first in many areas: the first TM tool for Windows; the first TM tool to directly integrate into Microsoft Word; the first 32-bit TM tool (Déjà Vu version 2.0); and the first affordable professional translation tool. In the following year, Eurolang Optimizer, a computer-aided translation system, was developed by Eurolang in France. Its components included the translator’s workstation, pre- translation server with translation memory and terminology database, and project management tool for multiple languages and users (Brace 1994). In Germany, Trados GmbH announced the release of the new Windows version of Translator’s Workbench, which could be used with standard Windows word processing packages via the Windows DDE interface (Brace 1994). In June 1994 Trados released S. Chan Development of translation technology9 MultiTerm Professional 1.5 which was included in Translator’s Workbench, which had fuzzy search to deliver successful searches even when words were incorrectly spelt, a dictionary-style interface, faster searches through use of new highly compressed data algorithms, drag and drop content into word processor and integrated programming language to create powerful layouts (http://www.translationzone.com). In Hungary, Tibor Környei developed the WordFisher for Microsoft Word macro set. The programme was written in the WordBasic language. For translators, it resembled a translation memory programme, but provided a simpler interface in Word (Környei 2000). In 1995, Nero AG was founded in Germany as a manufacturer of CD and DVD application software. Later, the company set up Across Systems GmbH as a division, which developed and marketed a tool of the same name for corporate translation management (CTM) that supported the project and workflow manage ment of translations (Schmidt 2006; German 2009: 9–10). During the first half of 1996, when Windows 95 was in its final stages of beta testing, Atril Development S.L. in Spain began writing a new version of Déjà Vu − not just porting the original code to 32 bits, but adding a large number of important functionalities that had been suggested by the users. In October, Atril released Déjà Vu beta v2.0. It consisted of the universal editor, Déjà Vu Interactive (DVI), the Database Maintenance module with an alignment tool, and a full-featured Terminology Maintenance module (Wassmer 2007: 37–38). In the same year, Déjà Vu again was the first TM tool available for 32-bit Windows and shipped with a number of filters for DTP packages − including FrameMaker, Interleaf, and QuarkXPress − and provided extensive project management facilities to enable project managers to handle large, multi-file, multilingual projects. In 1997, developments in France and Germany deserve mentioning. In France, CIMOS released Arabic to English translation software An-Nakel El-Arabi, with features like machine translation, customized dictionary and translation memory. Because of its deep sentence analysis and semantic connections, An-Nakel Al-Arabi could learn new rules and knowledge. CIMOS had previously released English to Arabic translation software (MultiLingual 1997). In Germany, Trados GmbH released WinAlign as a visual text alignment tool as the first fully- fledged 32-bit application in Trados. Mircosoft decided to base its internal localization memory store on Trados and consequently acquired a share of 20 per cent in Trados (http://www. translationzone.com). The year 1998 marks a milestone in the development of translation technology in China and Taiwan. In Beijing, Beijing Yaxincheng Software Technology Co. Ltd. 北京雅信誠公司 was set up as a developer of translation software. It was the first computer-aided translation software company in China. In Taipei, the Inventec Corporation released Dr Eye 98 (譯典通) with instant machine translation, dictionaries and termbases in Chinese and English (http://www. dreye.com.tw). In the same year, the activities of SDL and International Communications deserve special mention. In the United Kingdom, SDL began to acquire and develop translation and localization software and hardware − both for its own use in client-specific solutions, and to be sold as free-standing commercial products. At the end of the year, SDL also released SDLX, a suite of translation memory database tools. SDLX was developed and used in-house at SDL, and therefore was a mature product at its first offering (Hall 2000; MultiLingual 1998). Another British company, International Communications, a provider of localization, translation and multilingual communications services, released ForeignDesk v5.0 with the full support of Trados Translator’s Workbench 2.0 and WinAlign, S-Tagger. Then, Lionbridge Technologies Inc. acquired it (known as Massachusetts-based INT’L.com at the transaction) and later in November 2001 decided to open-source the ForeignDesk suite free of charge under BSD 10 licence. ForeignDesk was originally developed by International Communications around 1995 (MultiLingual 2000).In June 1999, Beijing YaxinCheng Software Technology Co. Ltd. established Shida CAT Research Centre (實達 CAT 研究中心), which later developed Yaxin CAT Bidirectional v2.5 (Chan 2004: 338). In June, SJTU Sunway Software Industry Ltd. acquired one of the most famous CAT products in China at the moment − Yaxin CAT from Beijing YaxinCheng Software Technology Co. Ltd., and it released the Yaxin CAT v1.0 in August. The release of this software signified, in a small way, that the development of computer-aided systems was no longer a European monopoly. In France, the first version of Wordfast PlusTools suite of CAT (Computer-Assisted Translation) tools was developed. One of the developers was Yves A. Champollion, who incorporated Wordfast LLC later. There were only a few TM software packages available in the first version. It could be downloaded freely before 2002, although registration was required (http://www.wordfast.net/champollion.net). In the United States, MultiCorpora R&D Inc. was incorporated, which was exclusively dedicated to providing language technology solutions to enterprises, governments, and language service providers (http://www.multicorpora.com). In the United Kingdom, following the launch of SDL International’s translation database tool, SDLX, SDL announced SDL Workbench. Packaged with SDLX, SDL Workbench memorized a user’s translations and automatically offered other possible translations and terminology from a user’s translation database within the Microsoft Word environment. In line with its ‘open’ design, it was able to work with a variety of file formats, including Trados and pre-translated RTF files (MultiLingual 1999). The year 2000 was a year of activities in the industry. In China, Yaxin CAT v2.5 Bidirectional (English and Chinese) was released with new features like seventy-four topic-specific lexicons with six million terms free of charge, project analysis, project management, share translation memory online and simultaneous editing of machine output (Chen 2001). In Germany, OmegaT, a free (GPL) translation memory tool, was publicly released. The key features of OmegaT were basic (the functionality was very limited), free, open-source, cross-operation systems as it was programmed in Java (http://www.omegat.org; Prior 2003). In Ireland, Alchemy Software Development Limited announced the acquisition of Corel CATALYST™, which was designed to boost the efficiency and quality of globalizing software products and was used by over 200 software development and globalization companies worldwide (http://www.alchemysoftware.ie). In the United Kingdom, SDL International announced in April the release of SDLX 2.0, which was a new and improved version of SDLX 1.03 (http://www.sdl.com). It also released SDL Webflow for managing multilingual website content (http://www.sdlintl.com). In Germany, Trados relocated its headquarters to the United States in March and became a Delaware corporation. In France, Wordfast v3.0 was released in September. The on-the-fly tagging and un-tagging of HTML (HyperText Markup Language) files was a major breakthrough in the industry. Freelance translators could translate HTML pages without worrying about the technical hurdles. Not much happened in 2001. In Taiwan, Inventec Corporation released Dr Eye 2001, with new functions like online search engine, full-text machine translation from English to Chinese, machine translation from Japanese to Chinese and localization plug-in (Xu 2001). In the United Kingdom, SDL International released SDLX 4.0 with real-time translation, a flexible software licence and enhanced capabilities. In the United States, Trados announced the launch of Trados 5 in two versions, Freelance and Team (http://www.translationzone.com). S. Chan Development of translation technology11 In contrast, the year 2002 was full of activities in the industry. In North America, MultiCorpora R&D Inc. in Canada released MultiTrans 3, providing corpus-based translation support and language management solution. It also introduced a new translation technology called Advanced Leveraging Translation Memory (ALTM). This model provided past translations in their original context and required virtually no alignment maintenance to obtain superior alignment results. In the United States, Trados 5.5 (Trados Corporate Translation Solution™) was released. MultiCorpora released MultiTrans 3.0, which introduced an optional client-server add-on, so it could be used in a web-based, multi-user environment or as a standalone workstation. Version 3 supported TMX and was also fully Unicode compliant (Locke and Giguère 2002: 51). In Europe and the United Kingdom, SDL International released its new SDLX Translation Suite 4, and then later that year released the elite version of the suite. The SDLX Translation Suite features a modular architecture consisting of five to eight components: SDL Project Wizard, SDL Align, SDL Maintain, SDL Edit and SDL TermBase in all versions, and SDL Analyse, SDL Apply and SDLX AutoTrans in the Professional and Elite versions (Wassmer 2003). In Germany, MetaTexis Software and Services released in April the first official version 1.00 of MetaTexis (http://www.metatexis.com). In Asia, Huajian Corporation in China released Huajian IAT, a computer-aided translation system (http://www.hjtek.com). In Taiwan, Otek launched in July Transwhiz Power version (client/server structure), which aimed at enterprise customers (http://www.otek.com.tw). In Singapore, Heartsome Holdings Pte. Ltd. was founded to develop language translation technology (Garcia and Stevenson 2006: 77). North America and Europe were active in translation technology in 2003. In 2003, MultiCorpora R&D Inc. in Canada released MultiTrans 3.5 which had new and improved capabilities, including increased processing speed of automated searches, increased network communications speed, improved automatic text alignment for all languages, and optional corpus-based pre-translation. Version 3.5 also offered several new terminology management features, such as support for additional data types, additional filters, batch updates and added import and export flexibility, as well as full Microsoft Office 2003 compatibility, enhanced Web security and document analysis capabilities for a wider variety of document formats (MultiLingual 2003). In the United States, Trados 6 was launched in April and Trados 6.5 was launched in October with new features like auto concordance search, Word 2003 support and access to internet TM server (Wassmer 2004: 61). In Germany, MetaTexis version 2.0 was released in October with a new database engine. And MetaTexis version ‘Net/Office’ was released with new features that supported Microsoft PowerPoint and Excel files, Trados Workbench, and could be connected with Logoport servers (http://www.metatexis.com). In Russia, PROMT, a developer of machine translation products and services, released a new version @promt XT with new functions like processing PDF file formats, which made PROMT the first among translation software that supported PDF. Also, one of the editions, @promt Expert integrated translation memory solutions (Trados) and a proprietary terminology extraction system (http://www.promt.com). In France, Atril, which was originally founded in Spain but which relocated its group business to France in the late 1990s, released Déjà Vu X (Standard, Professional, Workgroup and Term Sever) (Harmsen 2008). Wordfast 4, which could import and translate PDF contents, was also released (http://www.wordfast.net). Some developers of machine translation systems also launched new versions with a translation memory component, such as LogoVista, An-Nabel El-Arabi and PROMT (http://www. 12 promt.com). Each of these systems was created with distinct philosophies in its design, offering its own solutions to problems and issues in the work of translation. This was aptly pointed out by Brace (1994):Eurolang Optimizer is based on an ambitious client / server architecture designed primarily for the management of large translation jobs. Trados Workbench, on the other hand, offers more refined linguistic analysis and has been carefully engineered to increase the productivity of single translators and small workgroups. (2) The development of more built-in functions Computer-aided translation systems of the first and second periods were usually equipped with basic components, such as translation memory, terminology management, and translation editor. In this period, more functions were developed and more components were gradually integrated into computer-aided translation systems. Of all the new functions developed, tools for alignment, machine translation, and project management were most significant. Trados Translator’s Workbench II, for example, incorporated T Align, later known as WinAlign, into its workstation, followed by other systems such as Déjà Vu, SDLX, Wordfisher, and MultiTrans. Machine translation was also integrated into computer-aided translation systems to handle segments not found in translation memories. IBM’s Translation Manager, for example, introduced its Logic-Based Machine Translation (LMT) to run on IBM mainframes and RS/6000 Unix systems (Brace 1993). The function of project management was also introduced by Eurolang Optimizer in 1994 to better manage translation memory and terminology databases for multiple languages and users (Brace 1992a). (3) The dominance of Windows Operating System Computer-aided translation systems created before 1993 were run either in the DOS system or OS/2 system. In 1993, the Windows versions of these systems were first introduced and they later became the dominant stream. For example, IBM and Trados GmbH released a Windows version of TM/2 and of Translator’s Workbench respectively in mid-1993. More Windows versions came onto the market, such as the preliminary version of ATRIL’s Déjà Vu 1.0 in June in Spain. Other newly released systems running on Windows include SDLX, ForeignDesk, Trans Suite 2000, Yaxin CAT, Across, MultiTrans, Huajian, and TransWhiz. (4) The support of more document formats Computer-aided translation systems of this period could handle more document formats directly or with filters, including Adobe InDesign, FrameMaker, HTML, Microsoft PowerPoint, Excel, Word, QuarkXPress, even PDF by 2003. Trados 6.5, for example, supported all the widely used file formats in the translation community, which allowed translators and translation companies to translate documents in Microsoft Office 2003 Word, Excel and PowerPoint, Adobe InDesign 2.0, FrameMaker 7.0, QuarkXPress 5, and PageMaker. (5) The support of translation of more languages Translation memory is supposed to be language-independent, but computer-aided translation systems developed in the early 1990s did not support all languages. In 1992, Translator S. Chan Development of translation technology13 Workbench Editor, for example, supported only five European languages, namely, German, English, French, Italian and Spanish, while IBM Translation Manager / 2 supported 19 languages, including Chinese, Korean and other OS/2 compatible character code sets. This was due largely to the contribution of Unicode, which provided the basis for the processing, storage, and interchange of text data in any language in all modern software, thereby allowing developers of computer-aided translation systems to gradually resolve obstacles in language processing, especially after the release of Microsoft Office 2000. Systems with Unicode support mushroomed, including Transit 3.0 in 1999, MultiTerm and WordFisher 4.2.0 in 2000, Wordfast Classic 3.34 in 2001, and Tr-AID 2.0 and MultiTrans 3 in 2002. (6) The dominance of Trados as a market leader As a forerunner in the field, Trados became a market leader in this period. As observed by Colin Brace, ‘Trados has built up a solid technological base and a good market position’ in its first decade. By 1994, the company had a range of translation software, including Trados Translator’s Workbench (Windows and DOS versions), MultiTerm Pro, MultiTerm Lite, and MultiTerm Dictionary. Its technology in translation memory and file format was then widely used in other computer-aided translation systems and its products were most popular in the industry. From the late 1990s, a few systems began to integrate Trados’s translation memory into their systems. In 1997, ProMemoria, for example, was launched with its translation memory component provided by Trados. In 1998, International Communications released ForeignDesk 5.0 with the full support of Trados Translator’s Workbench 2.0, WinAlign, and S-Tagger. In 1999, SDLX supported import and export formats such as Trados and tab- delimited and CSV files. In 2000, Trans Suite 2000 was released with the capacity to process Trados RTF file. In 2001, Wordfast 3.22 could directly open Trados TMW translation memories (Translator’s Workbench versions 2 and 3). In 2003, PROMT XT Export integrated Trados’s translation memory. In October 2003, MetaTexis ‘Net/Office’ 2.0 was released and was able to work with Trados Workbench. 2004–2013: A period of global development Advances in technology have given added capabilities to computer-aided translation systems. During the last nine years, while most old systems have been upgraded on a regular basis, close to thirty new systems have been released to the market. This situation has offered a wider range of choices for buyers to acquire systems with different packages, functions, operation systems, and prices. One of the most significant changes in this period is the addition of new computer-aided translation companies in countries other than those mentioned above. Hungary is a typical example. In 2004, Kilgray Translation Technologies was established by three Hungarian language technologists. The name of the company was made up of the founders’ surnames: Kis Balázs (KI), Lengyel István (L), and Ugray Gábor (GRAY). Later, the company launched the first version of MemoQ, an integrated Localization Environment (ILE), in 2005. MemoQ’s first version had a server component that enabled the creation of server projects. Products of Kilgray included MemoQ, MemoQ server, QTerm, and TM Repository (http://www.kilgray.com). Another example is Japan. In Japan, Rozetta Corporation released TraTool, a computer- aided translation system with translation memory, an integrated alignment tool, an integrated terminology tool and a user dictionary. The product is still commercially available but no major improvement has been made since its first version (http://www.tratool.com). 14 Yet another example is Poland, where AidTrans Soft launched its AidTrans Studio 1.00, a translation memory tool. But the company was discontinued in 2010 (http://www. thelanguagedirectory.com/translation/translation_software). New versions of computer-aided translation systems with new features are worth noting. In the United Kingdom, ATA launched a new Arabic Memory Translation system, Xpro7 which had the function of machine translation (http://www.atasoft.com). SDL Desktop Products, a division of SDL International, announced the launch of SDLX 2004. Its new features included TMX Certification, seamlessly integrating with Enterprise systems such as online terminology and multilingual workflow management, adaptation of new file formats, synchronized web- enabled TM, and Knowledge-based Translation (http://www.sdl.com). In the United States, Systran released Systran Professional Premium 5.0, which contained integrated tools such as integrated translation memory with TMX support, a Translator’s Workbench for post-editing and ongoing quality analysis (http://www.systransoft.com). Multilizer Inc., a developer of globalization technologies in the United States, released a new version of Multilizer, which included multi-user translation memory with Translation Memory Manager (TMM), a standalone tool for maintaining Multilizer Translation Memory contents. TMM allowed editing, adding and deleting translations, and also included a briefcase model for working with translations off-line (http://www.multilizer.com). In Ukraine, Advanced International Translations (AIT) started work on user-friendly translation memory software, later known as AnyMen, which was released in December 2008. In 2005, translation technology moved further ahead with new versions and new functions. In North America, MultiCorpora in Canada released MultiTrans 4, which built on the foundation of MultiTrans 3.7 and had a new alignment process that was completely automated (MultiLingual 2005d). Trados, incorporated in the United States, produced Trados 7 Freelance, which supported twenty additional languages, including Hindi. At an operating system level, Microsoft Windows 2000, Windows XP Home, Windows XP Professional, and Windows 2003 Server were supported. More file formats were now directly supported by TagEditor. MultiCorpora also introduced MultiTrans 4, which was designed to meet the needs of large organizations by providing the newest efficiencies for translators in the areas of text alignment quality, user-friendliness, flexibility and web access (http://www.multicorpora.com). In Europe, Lingua et Machina, a memory translation tool developer, released SIMILIS v1.4, its second-generation translation tool. SIMILIS uses linguistic parsers in conjunction with the translation memory paradigm. This function allowed for the automatic extraction of bilingual terminology from translated documents. Version 1.4 brought compatibility with the Trados translation memory format (Text and TMX) and a new language, German (MultiLingual 2005b). In Switzerland, STAR Group released Transit XV Service Pack 14. This version extended its capabilities with a number of new features and support of 160 languages and language versions, including Urdu (India) and Urdu (Pakistan). It supported Microsoft Word 2003 files and had MySpell dictionaries (MultiLingual 2005a). PROMT released @promt 7.0 translation software, which supported the integrated translation memory, the first of its kind among PROMT’s products (http://www.promt.com). In the United Kingdom, SDL Desktop Products released the latest version of its translation memory tool SDLX 2005, which expanded the Terminology QA Check and automatically checked source and translations for inconsistent, incomplete, partial or empty translations, corrupt characters, and consistent regular expressions, punctuation, and formatting. Language support had been added for Maltese, Armenian and Georgian, and the system could handle more than 150 languages (MultiLingual 2005c). In June, SDL International acquired Trados for £35 million. The acquisition provided extensive end-to-end technology and service S. Chan Development of translation technology15 solutions for global information assets (http://www.translationzone.com). In October, SDL Synergy was released as a new project management tool on the market.In Asia, Huajian Corporation in China released in June Huajian Multilingual IAT network version (華建多語 IAT 網絡版) and in October Huajian IAT (Russian to Chinese) standalone version (http://www.hjtrans.com). In July, Beijing Orient Yaxin Software Technology Co. Ltd. released Yaxin CAT 2.0, which was a suite including Yaxin CAT 3.5, CAM 3.5, Server, Lexicons, Translation Memory Maintenance and Example Base. In Singapore, Heartsome Holdings Pte. Ltd. released Heartsome Translation Suite, which was composed of three programs: an XLIFF Editor in which source files were converted to XLIFF format and translated; a TMX Editor that dealt with TMX files; and a Dictionary Editor that dealt with TBX files (Garcia and Stevenson 2006: 77). In Taiwan, Otek released Transwhiz 9.0 for English, Chinese and Japanese languages (http://www.otek.com.tw). Significant advances in translation technology were made in 2006 particularly in Europe, the United Kingdom, and the United States. In Europe, Across Systems GmbH in Germany released in September its Corporate Translation Management 3.5, which marked the start of the worldwide rollout of Across software (MultiLingual 2006a). In the United Kingdom, SDL International released in February SDL Trados 2006, which integrated with Translators Workbench, TagEditor, SDLX editing environments and SDL MultiTerm. It included new support for Quark, InDesign CS2 and Java (http://www.sdl.com). In the United States, MultiCorpora launched TextBase TM concept (http://www.multicorpora.com). Apple Inc. released in August AppleTrans, a text editor specially designed for translators, featuring online corpora which represented ‘translation memory’ accessible through documents. AppleTrans helped users localize web pages (http:// developer.apple.com). Lingotek, a language search engine developer in the United States, launched a beta version of a collaborative language translation service that enhanced a translator’s efficiency by quickly finding meaning-based translated material for re-use. Lingotek’s language search engine indexed linguistic knowledge from a growing repository of multilingual content and language translations, instead of web pages. Users could then access its database of previously translated material to find more specific combinations of words for re-use. Such meaning-based searching maintained better style, tone, and terminology. Lingotek ran completely within most popular web browsers, including initial support for Internet Explorer and Firefox. Lingotek supported Word, Rich Text Format (RTF), Open Office, HTML, XHTML and Excel formats, thereby allowing users to upload such documents directly into Lingotek. Lingotek also supported existing translation memory files that were TMX- compliant memories, thus allowing users to import TMX files into both private and public indices (MultiLingual 2006b). In 2007, Wordfast 5.5 was released in France. It was an upgrade from Wordfast 4, in which Mac support was completely overhauled. This version continued to offer translators collaboration community via a LAN. Each Wordfast licence granted users the ability to search Wordfast’s web-based TM and knowledge base, VLTM (http://www.wordfast.net). In Germany, a group of independent translators and programmers under the GNU GPL licence developed in October Anaphraseus, a computer-aided translation tool for creating, managing and using bilingual translation memories. Originally, Anaphraseus was developed to work with the Wordfast TM format, but it could also export and import files in TMX format (http:// anaphraseus.sourceforge.net). In Hungary, Kilgray Translation Technologies released in January MemoQ 2.0. The main theme for the new version was networking, featuring a new resource server. This server not only stored translation memory and term bases, but also offered the possibility of creating server projects that allowed for the easy distribution of work among 16 several translators and ensured productivity at an early stage of the learning curve. Improvements on the client side included support for XML and Adobe FrameMaker MIF file formats; improvements to all other supported file formats; and support for the Segmentation Rule eXchange standard, auto-propagation of translated segments, better navigation and over a hundred more minor enhancements (Multilingual 2007). In Russia, MT2007, a freeware, was developed by a professional programmer Andrew Manson. The main idea was to develop easy- to-use software with extensive features. This software lacked many features that leading systems had. In the United Kingdom, SDL International released in March SDL Trados 2007, which had features such as a new concept of project delivery and supply chain, new one-central-view dashboard for new project wizard, PerfectMatch, automated quality assurance checker and full support for Microsoft Office 2007 and Windows Vista.In the United States, MultiCorpora’s Advanced Leveraging launched WordAlign which boasted the ability to align text at the individual term and expression level (http://www. multicorpora.com). MadCap Software Inc., a multi-channel content authoring company, developed in May MadCap Lingo, an XML-based, fully-integrated Help authoring tool and translation environment. MadCap Lingo offered an easy-to-use interface, complete Unicode support for all left-to-right languages for assisting localization tasks. Across Systems GmbH and MadCap Software announced a partnership to combine technical content creation with advanced translation and localization. In June, Alchemy Software Development Ltd. and MadCap Software, Inc. announced a joint technology partnership that combined technical content creation with visual TM technology. In 2008, Europe again figured prominently in computer-aided translation software production. In Germany, Across Systems GmbH released in April Across Language Server 4.0 Service Pack 1, which contained various extensions in addition to authoring, such as FrameMaker 8 and SGML support, context matching, and improvements for web-based translations via crossWeb (MultiLingual 2008a). It also introduced in July its new Language Portal Solution (later known as Across Language Portal) for large-scale organizations and multinational corporations, which allowed customers operating on an international scale to implement Web portals for all language-related issues and for all staff levels that need to make use of language resources. At the same time Across released the latest update to the Across Language Server, offering many new functions for the localization of software user interfaces (http://www.across.net). In Luxembourg, Wordbee S.A. was founded as a translation software company focusing on web-based integrated CAT and management solutions (http://www. wordbee.com). In Eastern Europe, Kilgray Translation Technologies in Hungary released in September MemoQ 3.0, which included a new termbase and provided new terminology features. It introduced full support for XLIFF as a bilingual format and offered the visual localization of RESX files. MemoQ 3.0 was available in English, German, Japanese and Hungarian (http:// kilgray.com). In Russia, Promt released in March 8.0 version with major improvement in its translation engine, translation memory system with TMX files import support, and dialect support in English (UK and American), Spanish (Castilian and Latin American), Portuguese (Portuguese and Brazilian), German (German and Swiss) and French (French, Swiss, Belgian, Canadian) (http://www.promt.com). In Ukraine, Advanced International Translations (AIT) released in December AnyMen, a translation memory system compatible with Microsoft Word. In Uruguay, Maxprograms launched in April Swordfish version 1.0-0, a cross-platform computer-aided translation tool based on the XLIFF 1.2 open standard published by OASIS (http://www.maxprograms.com). In November, this company released Stingray version 1.0-0, a cross-platform document aligner. The translation memories in TMX, CSV or Trados S. Chan Development of translation technology17 TXT format generated by Stingray could be used in most modern computer-aided translation systems (http://www.maxprograms.com).In Ireland, Alchemy Software Development, a company in visual localization solutions, released in July Alchemy PUBLISHER 2.0, which combined visual localization technology with translation memory for documentation. It supported standard documentation formats, such as MS Word, XML, application platforms such as Windows 16/22/64x binaries, web- contents formats such as HTML, ASP, and all derivative content formats (http://www. alchemysoftware.ie). In North America, JiveFusion Technologies, Inc. in Canada officially launched Fusion One and Fusion Collaborate 3.0. The launches introduced a new method of managing translation memories. New features included complete contextual referencing. JiveFusion also integrated Fusion Collaborate 3.0 with TransFlow, a project and workflow management solution by Logosoft (MultiLingual 2008b). In the United States, MadCap Software, Inc. released in February MadCap Lingo 2.0, which included the Darwin Information Typing Architecture standard, Microsoft Word and a range of standard text and language formats. In September, it released MadCap Lingo 3.0, which included a new project packager function designed to bridge the gap between authors and translators who used other translation memory system software and a new TermBase Editor for creating databases of reusable translated terms. In Asia, Yaxin CAT 4.0 was released in China in August with some new features including a computer-aided project platform for project management and huge databases for handling large translation projects. In Taiwan, Otek released Transwhiz 10 for translating English, Chinese and Japanese languages, with fuzzy search engine and Microsoft Word workstation (http://www.otek.com.tw). The year 2009 witnessed the development of Autshumato Integrated Translation Environment (ITE) version 1.0, a project funded by the Department of Arts and Culture of the Republic of South Africa. It was released by The Centre for Text Technology (CTexT®) at the Potchefstroom Campus of the North-West University and University of Pretoria after two years of research and development. Although Autshumato ITE was specifically developed for the eleven official South African languages, it was in essence language independent, and could be adapted for translating between any language pair. In Europe, Wordfast released in January Wordfast Translation Studio, a bundled product with Wordfast Classic (for Microsoft Word) and Wordfast Pro (a standalone CAT platform). With over 15,000 licences in active use, Wordfast claimed itself the second most widely used translation memory tool (http://www.wordfast.net). In Germany, Across Systems GmbH released in May Across Language Server 5.0, which offered several options for process automation as well as for workflow management and analysis. Approximately fifty connections were available for interacting with other systems (MultiLingual 2009b). In September, STAR Group in Switzerland released Transit NXT (Professional, Freelance Pro, Workstation, and Freelance). Service pack 1 for Transit NXT/TermStar NXT contained additional user interface languages for Chinese, Spanish, Japanese, and Khmer, enhanced alignment usability, support for QuarkXpress 7, and proofreading for internal repetitions. In the United Kingdom, SDL announced in June the launch of SDL Trados® Studio 2009 in the same month, which included the latest versions of SDL MultiTerm, SDL Passolo Essential, SDL Trados WinAlign, and SDL Trados 2007 Suite. New features included Context Match, AutoSuggest, QuickPlace (http://www.sdl.com). In October, SDL released its enterprise platform SDL TM Server™ 2009, a new solution to centralize, share, and control translation memories (http://www.sdl.com). 18 In North America, JiveFusion Technologies Inc. in Canada released in March Fusion 3.1 to enhance current TMX compatibility and the capability to import and export to TMX while preserving the complete segment context (MultiLingual 2009a). In the United States, Lingotek introduced software-as-a-service collaborative translation technology which combined the workflow and computer-aided translation capabilities of human and machine translation into one application. Organizations could upload new projects, assign translators (paid or unpaid), check the status of current projects in real time and download completed documents from any computer with web access (MultiLingual 2009c). In Asia, Beijing Zhongke LongRay Software and Technology Ltd. Co. in China released in September LongRay CAT 3.0 (standalone edition), a CAT system with translation memory, alignment, dictionary and terminology management and other functions (http://www.zklr. com). In November, Foshan Snowman Computer Co. Ltd. released Snowman version 1.0 in China (http://www.gcys.cn). Snowman deserves some mentioning because (1) it was new; (2) the green trial version of Snowman could be downloaded free of charge; (3) it was easy to use as its interface was user-friendly and the system was easy to operate; and (4) it had the language pair of Chinese and English, which caters to the huge domestic market as well as the market abroad. Most of the activities relating to computer-aided translation in 2010 took place in Europe and North America. In Germany, Across Systems GmbH released in August Across Language Server v. 5 Service Pack 1, which introduced a series of new functionalities and modes of operation relating to the areas of project management, machine translation, crowdsourcing and authoring assistance (http://new.multilingual.com). In October, MetaTexis version 3.0 was released, which imported filter for Wordfast Pro and Trados Studio translation memories and documents (http://www.metatexis.com). In France, Wordfast LLC released in July Wordfast Pro 2.4 (WFP) with over sixty enhancements. This system was a standalone environment that featured a highly customizable interface, enhanced batch processing functionality, and increased file format support (http://www.wordfast.net). In October, Wordfast LLC created an application to support translation on the iPhone and iPad in the Wordfast Anywhere environment (http:// www.wordfast.net). In Hungary, Kilgray Translation Technologies released in February MemoQ 4.0, which was integrated with project management functions for project managers who wanted to have more control and enable translators to work in any translation tool. In October, the company released MemoQ 4.5, which had a rewritten translation memory engine and improvements to the alignment algorithm (http://www.kilgray.com). In France, Atril released in March TeaM Server, which allowed translators with Déjà Vu Workgroup to work on multinational and multisite translation projects on a LAN or over the Internet, sharing their translations in real-time, ensuring superior quality and consistency. TeaM Server also provided scalable centralized storage for translation memories and terminology databases. The size of translation repositories and the number of concurrent users were only limited by the server hardware and bandwidth (http://www.atril.com). In October, Atril released Déjà Vu X2 in four editions: Editor, Standard, Professional, and Workgroup. Its new features included DeepMiner data extraction engine, new StartView interface, and AutoWrite word prediction. In Switzerland, STAR Group released in October Transit NXT Service Pack 3 and TermStar NXT. Transit NXT Service Pack 3 contained the following improvements: support of Microsoft Office 2007, InDesign CS5, QuarkXpress 8 and QuarkXpress 8.1, and PDF synchronization for MS Word files. In the United Kingdom, SDL released in March a new subscription level of its SDL Trados Studio, which included additional productivity tools for translators such as Service Pack 2, enabling translators to plug in to multiple automatic translation tools. The company also did a S. Chan Development of translation technology19 beta launch of SDL OpenExchange, inviting the developer community to make use of standard open application programming interfaces to increase the functionality of SDL Trados Studio (Multilingual 2010a). In September, XTM International released XTM Cloud, which was a totally online Software-as-a-Service (SaaS) computer-assisted translation tool set, combining translation workflow with translation memory, terminology management and a fully featured translator workbench. The launch of XTM Cloud means independent freelance translators have access to XTM for the first time (http://www.xtm-intl.com). In Ireland, Alchemy Software Development Limited released in May Alchemy PUBLISHER 3.0, which supports all aspects of the localization workflow, including form translation, engineering, testing, and project management. It also provided a Machine Translation connector which was jointly developed by PROMT, so that documentation formats could be machine translated (http:// www.alchemysoftware.ie; http://www.promt.com). In North America, IBM in the United States released in June the open source version of OpenTM/2, which originated from the IBM Translation Manager. OpenTM/2 integrated with several aspects of the end-to-end translation workflow (http://www.opentm2.org). Partnering with LISA (Localization Industry Standards Association), Welocalize, Cisco, and Linux Solution Group e.V. (LiSoG), IBM aimed to create an open source project that provided a full-featured, enterprise-level translation workbench environment for professional translators on OpenTM/2 project. According to LISA, OpenTM/2 not only provided a public and open implementation of translation workbench environment that served as the reference implementation of existing localization industry standards, such as TMX, it also aimed to provide standardized access to globalization process management software (http://www.lisa. org; LISA 2010). Lingotek upgraded in July its Collaborative Translation Platform (CTP) to a software-as-a-service product which combined machine translation, real-time community translation, and management tools (MultiLingual 2010b). MadCap Software, Inc. released in September MadCap Lingo v4.0, which had a new utility for easier translation alignment and a redesigned translation editor. Systran introduced in December Desktop 7 Product Suite, which included the Premium Translator, Business Translator, Office Translator, and Home Translator. Among them, Premium Translator and Business Translator were equipped with translation memory and project management features. In South America, Maxprograms in Uruguay released in April Swordfish II, which incorporated Anchovy version 1.0-0 as glossary manager and term extraction tool, and added support for SLD XLIFF files from Trados Studio 2009 and Microsoft Visio XML Drawings, etc. (http://www.maxprograms.com). In 2011, computer-aided translation was active in Europe and America. In Europe, ATRIL / PowerLing in France released in May Déjà Vu X2, a new version of its computer-assisted translation system, which had new features such as DeepMiner data mining and translation engine, SmartView Interface and a multi-file and multi-format alignment tool (MultiLingual 2011). In June, Wordfast Classic v6.0 was released with features such as the ability to share TMs and glossaries with an unlimited number of users, improved quality assurance, AutoComplete, and improved support for Microsoft Word 2007/2010 and Mac Word 2011 (http://www.wordfast.net). In Luxembourg, the Directorate-General for Translation of the European Commission released in January its one million segments of multilingual Translation Memory in TMX format in 231 language pairs. Translation units were extracted from one of its large shared translation memories in Euramis (European Advanced Multilingual Information System). This memory contained most, but not all, of the documents of the Acquis Communautaire, the entire body of European legislation, plus some other documents which were not part of the Acquis. In Switzerland, the STAR Group released 20 in February Service Pack 4 for Transit NXT and TermStar NXT. Transit NXT Service Pack 4 contained the following improvements: support of MS Office 2010, support of Quicksilver 3.5l, and preview for MS Office formats. In Eastern Europe, Kilgray Translation Technologies in Hungary released in June TM Repository, the world’s first tool-independent Translation Memory management system (http://kilgray.com). Kilgray Translation Technologies later released MemoQ v 5.0 with the AuditTrail concept to the workflow, which added new improvements like versioning, tracking changes (to show the difference of two versions), X-translate (to show changes on source texts), the Post Translation Analysis on formatting tags (Kilgray Translation Technologies 2011). In the United Kingdom, XTM International released in March XTM 5.5, providing both Cloud and On-Premise versions, which contained customizable workflows, a new search and replace feature in Translation Memory Manager and the redesign of XTM Workbench (http:// www.xtm-intl.com). In North America, MultiCorpora R&D Inc. in Canada released in May MutliTrans Prism, a translation management system (TMS) for project management, translation memory and terminology management (MultiCorpora 2011). In 2012, the development of computer-aided translation in various places was considerable and translation technology continued its march to globalization. In North America, the development of computer-aided translation was fast. In Canada, MultiCorpora, a provider of multilingual asset management solutions, released in June MultiTrans Prism version 5.5. The new version features a web editing server that extends control of the management of translation processes, and it can be fully integrated with content management systems. In September, Terminotix launched LogiTerm 5.2. Its upgrades and new features, including indexing TMX files directly in Bitext database, reinforced the fuzzy match window, and adjusted buttons (http://terminotix.com/news/newsletter). In December, MultiCorpora added new machine translation integrations to its MultiTrans Prism. The integration options include Systran, Google and Microsoft (http://www.multicorpora.com). In Asia, there was considerable progress in computer-aided translation in China. Transn Information Technology Co., Ltd. released TCAT 2.0 as freeware early in the year. New features of this software include the Translation Assistant (翻譯助理) placed at the sidebar of Microsoft Office, pre-translation with TM and termbase, source segment selection by highlighting (自動取句) (http://www.transn.com). In May, Foshan Snowman Computer Co. Ltd. released Snowman 1.27 and Snowman Collaborative Translation Platform (雪人 CAT 協 同翻譯平臺) free version. The platform offers a server for a central translation memory and termbase so that all the users can share their translations and terms, and the reviewers can view the translations simultaneously with translators. It also supports online instant communication, document management and online forum (BBS) (http://www.gcys.cn). In July, Chengdu Urelite Tech Co. Ltd. (成都優譯信息技術有限公司), which was founded in 2009, released Transmate, including the standalone edition (beta), internet edition and project management system. The standalone edition was freely available for download from the company’s website. The standalone edition of Transmate is targeted at freelancers and this beta release offers basic CAT functions, such as using TM and terminology during translation. It has features such as pre-translation, creating file-based translation memory, bilingual text export and links to an online dictionary website and Google MT (http://www.urelitetech.com.cn). Heartsome Translation Studio 8.0 was released by the Shenzhen Office of Heartsome in China. Its new features include pre-saving MT results and external proofreading file export in RTF format. The new and integrated interface also allows the user to work in a single unified environment in the translation process (http://www.heartsome.net). S. Chan Development of translation technology21 In Japan, Ryan Ginstrom developed and released Align Assist 1.5, which is freeware to align source and translation files to create translation memory. The main improvement of this version is the ability to set the format of a cell text (http://felix-cat.com). In October, LogoVista Corporation released LogoVista PRO 2013. It can support Microsoft Office 2010 64-bit and Windows 8. More Japanese and English words are included and the total number of words in dictionaries is 6.47 million (http://www.logovista.co.jp). In Europe, the developments of computer-aided translation systems are noteworthy. In the Czech Republic, the MemSource Technologies released in January MemSource Editor for translators as a free tool to work with MemSource Cloud and MemSource Server. The Editor is multiplatform and can be currently installed on Windows and Macintosh (http:// www.memsource.com). In April, this company released MemSource Cloud 2.0. MemSource Plugin, the former CAT component for Microsoft Word, is replaced by the new MemSource Editor, a standalone translation editor. Other new features include adding comments to segments, version control, translation workflow (only in the Team edition), better quality assurance and segmentation (http://blog.memsource.com). In December, MemSource Technologies released MemSource Cloud 2.8. It now encrypts all communication by default. This release also includes redesigned menu and tools. Based on the data about previous jobs, MemSource can suggest relevant linguistics for translation jobs (http://www.memsource.com). In France, Wordfast LLC released Wordfast Pro 3.0 in April. Its new features include bilingual review, batch TransCheck, filter 100 per cent matches, split and merge TXML files, reverse source/target and pseudo-translation (http://www.wordfast.com). In June Atril and PowerLing updated Déjà Vu X2. Its new features include an incorporated PDF converter and a CodeZapper Macro (http://www.atril.com). In Germany, Across Language Server v 5.5 was released in November. New features such as linguistic supply chain management are designed to make project and resources planning more transparent. The new version also supports the translation of display texts in various formats, and allows the protection of the translation units to ensure uniform use (http://www.across.net). In Hungary, Kilgray Translation Technologies released in July MemoQ 6.0 with new features like predictive typing and several new online workflow concepts such as FirstAccept (assign job to the first translator who accepted it on the online workflow), GroupSourcing, Slicing, and Subvendor group (http://kilgray.com). In December, the company released MemoQ 6.2. Its new features include SDL package support, InDesign support with preview, new quality assurance checks and the ability to work with multiple machine translation engines at the same time (http://kilgray.com). In Luxembourg, Wordbee in October designed a new business analysis module for its Wordbee translation management system, which provides a new dashboard where over 100 real-time reports are generated for every aspect of the localization process (http://www.wordbee.com). In Switzerland, STAR Group released Service Pack 6 for Transit NXT and TermStar NXT . The improvements of Service Pack 6 of Transit NXT contain the support of Windows 8 and Windows Server 2012, QuarkXPress 9.0-9.2, InDesign CS6, integrated OpenOffice spell check dictionaries, 10 additional Indian languages (http://www.star-group.net). In the United Kingdom, XTM International, a developer of XML authoring and translation tools, released in April XTM Suite 6.2. Its updates include a full integration with machine translation system, Asia Online Language Studio and the content management system XTRF. In October, the company released XTM Suite 7.0 and a new XTM Xchange module in XTM Cloud intended to increase the supply chain. Version 7.0 includes project management enhancements, allowing users to group files, assign translators to specific groups or languages, and create different workflows for different languages (http://www.xtm-intl.com). 22 During this period, the following trends are of note. 1 The systematic compatibility with Windows and Microsoft Office Of the sixty-seven currently available systems on the market, only one does not run on the Windows operation systems. Computer-aided translation systems have to keep up with the advances in Windows and Microsoft Office for the sake of compatibility. Wordfast 5.51j, for example, was released in April 2007, three months after the release of Windows Vista, and Wordfast 5.90v was released in July 2010 to support Microsoft Office Word 2007 and 2010. 2 The integration of workflow control into CAT systems Besides re-using or recycling translations of repetitive texts and text-based terminology, systems developed during this period added functions such as project management, spell check, quality assurance, and content control. Take SDL Trados Studio 2011 as an example. This version, which was released in September 2011, has a spell checking function for a larger number of languages and PerfectMatch 2.0 to track changes of the source documents. Most of the systems on the market can also perform ‘context match’, which is the identical match with identical surrounding segments in the translation document and in the translation memory. 3 The availability of networked or online systems Because of the fast development of new information technologies, most CAT systems during this period were server-based, web-based and even cloud-based CAT systems, which had a huge storage of data. By the end of 2012, there were fifteen cloud-based CAT systems available on the market for individuals or enterprises, such as Lingotek Collaborative Translation Platform, SDL World Server, and XTM Cloud. 4 The adoption of new formats in the industry Data exchange between different CAT systems has always been a difficult issue to handle as different systems have different formats, such as dvmdb for Déjà Vu X, and tmw for SDL Trados Translator’s Workbench 8.0. These program-specific formats cannot be mutually recognizable, which makes it impossible to share data in the industry. In the past, the Localization Industry Standards Association (LISA) played a significant role in developing and promoting data exchange standards, such as SRX (Segmentation Rules eXchange), TMX (Translation Memory eXchange), TBX (Term-Bese eXchange) and XLIFF (XML Localisation Interchange File Format). (http://en.wikipedia.org/wiki/XLIFF). It can be estimated that the compliance of industry standards is also one of the future directions for better data exchange. Translation technology on a fast track: a comparison of the developments of computer-aided translation with human translation and machine translation The speed of the development of translation technology in recent decades can be illustrated through a comparison of computer-aided translation with human translation and machine translation. The development of human translation Human translation, in comparison with machine translation and computer-aided translation, has taken a considerably longer time and slower pace to develop. The history of human translation can be traced to 1122 bc when during the Zhou dynasty (1122–255 bc), a foreign S. Chan Development of translation technology23 affairs bureau known as Da xing ren 大行人 was established to provide interpreting services for government officials to communicate with the twelve non-Han minorities along the borders of the Zhou empire (Chan 2009: 29−30). This is probably the first piece of documentary evidence of official interpreting in the world. Since then a number of major events have taken place in the world of translation. In 285 bc, there was the first partial translation of the Bible from Hebrew into Greek in the form of the Septuagint (Worth 1992: 5−19). In 250 bc, the contribution of Andronicus Livius to translation made him the ‘father of translation’ (Kelly 1998: 495−504). In 67, Zhu Falan made the first translation of a Buddhist sutra in China (Editorial Committee 1988: 103). In 1141, Robert de Retines produced the first translation of the Koran in Latin (Chan 2009: 47). In 1382, John Wycliffe made the first complete translation of the Bible in English (Worth 1992: 66−70). In 1494, William Tyndale was the first scholar to translate the Bible from the original Hebrew and Greek into English (Delisle and Woodsworth 1995: 33−35). In 1611, the King James Version of the Bible was published (Allen 1969). In 1814, Robert Morrison made the first translation of the Bible into Chinese (Chan 2009: 73). In 1945, simultaneous interpreting was invented at the Nuremberg Trials held in Germany (Gaiba 1998). In 1946, the United Bible Societies was founded in New York (Chan 2009: 117). In 1952, the first conference on machine translation was held at the Massachusetts Institute of Technology (Hutchins 2000: 6, 34−35). In 1953, the Fédération Internationale des Traducteurs (FIT), or International Association of Translators, and the Association Internationale des Interprètes de Conference (AIIC), or the International Association of Conference Interpreters, were both founded in Paris (Haeseryn 1989: 379−84; Phelan 2001). In 1964, with the publication of Toward a Science of Translating in which the concept of dynamic equivalent translation was proposed, Eugene A. Nida was referred to as the ‘Father of Translation Theory’ (Nida 1964). In 1972, James S. Holmes proposed the first framework for translation studies (Holmes 1972/1987: 9−24, 1988: 93−98). In 1978, Even-Zohar proposed the Polysystem Theory (Even-Zohar 1978: 21−27). A total of some seventeen major events took place during the history of human translation, which may be 3,135 years old. This shows that in terms of the mode of production, human translation has remained unchanged for a very long time. The development of machine translation In comparison with human translation, machine translation has advanced enormously since its inception in the 1940s. This can be clearly seen from an analysis of the countries with research and development in machine translation during the last seventy years. Available information shows that an increasing number of countries have been involved in the research and development of machine translation. This is very much in evidence since the beginning of machine translation in 1947. Actually, long before the Second World War was over and the computer was invented, Georges Artsrouni, a French-Armenian engineer, created a translation machine known as ‘Mechanical Brain’. Later in the year, Petr Petrovi č Smirnov- Troyanskij (1894−1950), a Russian scholar, was issued a patent in Moscow on 5 September for his construction of a machine which could select and print words while translating from one language into another or into several others at the same time (Chan 2004: 289). But it was not until the years after the Second World War that the climate was ripe for the development of machine translation. The invention of computers, the rise of information theory, and the advances in cryptology all indicated that machine translation could be a reality. In 1947, the idea of using machines in translating was proposed in March by Warren Weaver (1894−1978), who was at that time the vice president of the Rockefeller Foundation, and 24 Andrew D. Booth of Birkbeck College of the University of London. They wanted to make use of the newly invented computer to translate natural languages. Historically speaking, their idea was significant in several ways. It was the first application of the newly invented computers to non-numerical tasks, i.e. translation. It was the first application of the computer to natural languages, which was later to be known as computational linguistics. It was also one of the first areas of research in the field of artificial intelligence. The following year witnessed the rise of information theory and its application to translation studies. The role of this theory has been to help translators recognize the function of concepts such as information load, implicit and explicit information, and redundancy (Shannon and Weaver 1949; Wiener 1954). On 15 July 1948, Warren Weaver, director of the Rockefeller Foundation’s natural sciences division, wrote a memorandum for peer review outlining the prospects of machine translation, known in history as ‘Weaver’s Memorandum’, in which he made four proposals to produce translations better than word-for-word translations (Hutchins 2000: 18−20). The first machine translation system, the Georgetown-IBM system for Russian−English translation, was developed in the United States in June 1952. The system was developed by Leon Dostert and Paul Garvin of Georgetown University and Cuthbert Hurd and Peter Sheridan of IBM Corporation. This system could translate from Russian into English (Hutchins 1986: 70−78). Russia was the second country to develop machine translation. At the end of 1954, the Steklov Mathematical Institute of the Academy of Sciences began work on machine translation under the directorship of Aleksej Andreevi č Ljapunov (1911−1973), a mathematician and computer expert. The first system developed was known as FR-I, which was a direct translation system and was also considered one of the first generation of machine translation systems. The system ran on STRELA, one of the first generation of computers (Hutchins 2000: 197−204). In the same year, the United Kingdom became the third country to engage in machine translation. A research group on machine translation, Cambridge Language Research Group, led by Margaret Masterman, was set up at Cambridge University, where an experimental system was tried on English-French translation (Wilks 2000: 279−298). In 1955, Japan was the fourth country to develop machine translation. Kyushu University was the first university in Japan to begin research on machine translation (Nagao 1993: 203−208). This was followed by China, which began research on machine translation with a Russian− Chinese translation algorithm jointly developed by the Institute of Linguistics and the Institute of Computing Technology (Dong 1988: 85−91; Feng 1999: 335−340; Liu 1984: 1−14). Two years later, Charles University in Czechoslovakia began to work on English–Czech machine translation (http://www.cuni.cz). These six countries were the forerunners in machine translation. Other countries followed suit. In 1959, France set up the Centre d’Études de la Traduction Automatique (CETA) for machine translation (Chan 2009: 300). In 1960, East Germany had its Working Group for Mathematical and Applied Linguistics and Automatic Translation, while in Mexico, research on machine translation was conducted at the National Autonomous University of Mexico (Universidad Nacional Autonoma de Mexico) (http://www.unam.mx). In 1962, Hungary’s Hungarian Academy of Sciences conducted research on machine translation. In 1964 in Bulgaria, the Mathematical Institute of the Bulgarian Academy of Sciences in Sofia set up the section of ‘Automatic Translation and Mathematical Linguistics’ to conduct work on machine translation (http://www.bas.bg; Hutchins 1986: 205−06). In 1965, the Canadian Research Council set up CETADOL (Centre de Traitement Automatique des Données Linguistiques) to work on an English−French translation system (Hutchins 1986: 224). S. Chan Development of translation technology25 But with the publication of the ALPAC Report prepared by the Automatic Language Processing Advisory Committee of the National Academy of Sciences, which concluded with the comment that there was ‘no immediate or predictable prospect of useful machine translation’, funding for machine translation in the United States was drastically cut and interest in machine translation waned considerably (ALPAC 1966; Warwick 1987: 22−37). Still, sporadic efforts were made in machine translation. An important system was developed in the United States by Peter Toma, previously of Georgetown University, known as Systran, an acronym for System Translation. To this day, this system is still one of the most established and popular systems on the market. In Hong Kong, The Chinese University of Hong Kong set up the Hung On-To Research Laboratory for Machine Translation to conduct research into machine translation and developed a practical machine translation system known as ‘The Chinese University Language Translator’, abbreviated as CULT (Loh 1975: 143−155, 1976a: 46−50, 1976b: 104−05; Loh, Kong and Hung 1978: 111−120; Loh and Kong 1979: 135−148). In Canada, the TAUM group at Montreal developed a system for translating public weather forecasts known as TAUM-METEO, which became operative in 1977. In the 1980s, the most important translation system developed was the EUROTRA system, which could translate all the official languages of the European Economic Community (Arnold and Tombe 1987: 1143−1145; Johnson, King and Tombe 1985: 155−169; King 1982; King 1987: 373−391; Lau 1988: 186−191; Maegaard 1988: 61−65; Maegaard and Perschke 1991: 73−82; Somers 1986: 129−177; Way, Crookston and Shelton 1997: 323−374). In 1983, Allen Tucker, Sergei Nirenburg, and others developed at Colgate University an AI-based multilingual machine translation system known as TRANSLATOR to translate four languages, namely English, Japanese, Russian, and Spanish. This was the beginning of knowledge-based machine translation in the United States (http://www.colgate.edu). The following year, Fujitsu produced ATLAS/I and ATLAS/II translation systems for translation between Japanese and English in Japan, while Hitachi and Market Intelligence Centre (QUICK) developed the ATHENE English−Japanese machine translation system (Chan 2009: 223). In 1985, the ArchTran machine translation system for translation between Chinese and English was launched in Taiwan and was one of the first commercialized English−Chinese machine translation systems in the world (Chen, Chang, Wang and Su 1993: 87−98). In the United States, the METAL (Mechanical Translation and Analysis of Languages) system for translation between English and German, supported by the Siemens Company in Munich since 1978 and developed at the University of Texas, Austin, became operative (Deprez, Adriaens, Depoortere and de Braekeleer 1994: 206−212; Lehmann, Bennett and Slocum et al. 1981; Lehrberger 1981; Little 1990: 94−107; Liu and Liro 1987: 205−218; Schneider 1992: 583−594; Slocum, Bennett, Bear, Morgan and Root 1987: 319−350; White 1987: 225−240). In China, the TranStar English−Chinese Machine Translation System, the first machine-translation product in China, developed by China National Computer Software and Technology Service Corporation, was commercially available in 1988 (http://www.transtar.com.cn). In Taiwan, the BehaviorTran, an English−Chinese machine translation system, was also launched in the same year. In the 1990s, Saarbrucken in Germany formed the largest and the most established machine translation group in 1996. The SUSY (Saarbrücker Ubersetzungssystem/The Saarbrücken Machine Translation System) project for German to English and Russian to German machine translation was developed between 1972 and 1986 (rz.uni-sb.de). In 1997, Dong Fang Kuai Che 東方快車 (Orient Express), a machine translation system developed by the China Electronic Information Technology Ltd. in China, was commercially available (Chan 2004: 336) while in Taiwan, TransBridge was developed for internet translation from English into Chinese (http:// 26 www.transbridge.com.tw). The first year of the twenty-first century witnessed the development of BULTRA (BULgarian TRAnslator), the first English−Bulgarian machine translation tool, by Pro Langs in Bulgaria (Chan 2004: 339).What has been presented above shows very clearly that from the beginning of machine translation in 1947 until 1957, six countries were involved in the research and development of machine translation, which included Massachusetts Institute of Technology and Georgetown University in the United States in 1952, Academy of Sciences in Russia and Cambridge University in the United Kingdom in 1954, Kyushu University in Japan in 1955, the Institute of Linguistics in China in 1956, and Charles University in Czechoslovakia in 1957. By 2007, it was found that of the 193 countries in the world, 30 have conducted research on computer or computer-aided translation, 9 actively. This means that around 16 per cent of all the countries in the world have been engaged in machine translation, 30 per cent of which are active in research and development. The 31 countries which have been engaged in the research and development of machine translation are: Belgium, Brazil, Bulgaria, Canada, China, Czechoslovakia, Denmark, Finland, France, Germany, Hungary, India, Italy, Japan, Korea, Macau, Malaysia, Mexico, the Netherlands, Luxemburg, Poland, Russia, Singapore, Slovenia, Spain, Sweden, Switzerland, Taiwan, Tunisia, the United Kingdom, and the United States. Of these, the most active countries are China and Japan in Asia, France, Germany, the Netherlands, the United Kingdom, and Russia in Europe, and Canada and the United States in North America. The huge increase in the number of countries engaged in machine translation and the fast development of systems for different languages and language pairs show that machine translation has advanced by leaps and bounds in the last 65 years. Conclusion It should be noted that computer-aided translation has been growing rapidly in all parts of the world in the last 47 years since its inception in 1967. Drastic changes have taken place in the field of translation since the emergence of commercial computer-aided translation systems in the 1980s. In 1988, as mentioned above, we only had the Trados system that was produced in Europe. Now we have more than 100 systems developed in different countries, including Asian countries such as China, Japan, and India, and the northern American countries, Canada and the United States. In the 1980s, very few people had any ideas about computer-aided translation, let alone translation technology. Now, it is estimated that there are around 200,000 computer-aided translators in Europe, and more than 6,000 large corporations in the world handle their language problems with the use of corporate or global management computer- aided translation systems. At the beginning, computer-aided translation systems only had standalone editions. Now, there are over seventeen different types of systems on the market. According to my research, the number of commercially available computer-aided translation systems from 1984 to 2012 is 86. Several observations on these systems can be made. First, about three computer-aided translation systems have been produced every year during the last 28 years. Second, because of the rapid changes in the market, nineteen computer-aided translation systems failed to survive in the keen competition, and the total number of current commercial systems stands at 67. 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Zhang, Zheng 張政 (2006)《計算機翻譯研究》(Studies on Machine Translation), Beijing: Tsinghua University Press 清華大學出版社. 32 2 COMPUTER-AIDED TRANSLATION Major concepts Chan Sin-wai the chinese university ff hfng kfng, hfng kfng, china Introduction When the term computer-aided translation is mentioned, we often associate it with the functions a computer-aided translation system offers, such as toolbars, icons, and hotkeys, the built-in tools we can use, such as online dictionaries, browsers, and the computational hitches we often encounter when working on a computer-aided translation project, such as chaotic codes. What is more important is to see beyond the surface of computer-aided translation to find out the major concepts that shape the development of functions in translation technology. Concepts, which are relatively stable, govern or affect the way functions are designed and developed, while functions, which are fast-changing, realize the concepts through the tasks they perform. As a major goal of machine translation is to help human translators, a number of functions in computer-aided translation systems have been created to enable machine processing of the source with minimum human intervention. Concepts, moreover, are related to what translators want to achieve in translating. Simply put, translators want to have a controllable (controllability) and customizable (customizability) system, which is compatible with file formats (compatibility) and language requirements, and behaves as well as (simulativity) or even better than (emulativity) a human translator, to allow them to work together (collaborativity) to produce quality translations (productivity). We have therefore identified seven major concepts which are important in computer-aided translation: simulativity, emulativity, productivity, compatibility, controllability, customizability, and collaborativity. The order in which concepts are arranged can be memorized more easily by their acronym SEPCCCC. Simulativity The first concept of computer-aided translation is simulativity, which is about the way in which a computer-aided translation system models the behaviour of a human translator by means of its functions, such as the use of concordancers in text analysis to model after comprehension on the part of the human translator and the creation of a number of quality assurance tools to follow the way checking is done by a human translator. There are a number of ways to illustrate man–machine simulativity. Computer-aided translation33 (1) Goal of translation The first is about the ultimate goal of translation technology. All forms of translation (machine translation, computer-aided translation and human translation) aim at obtaining high-quality translations. In the case of machine translation, the goal of a fully automatic high-quality translation (FAHQT) is to be achieved through the use of a machine translation system without human intervention. In the case of computer-aided translation, the same goal is to be achieved through a computer-aided translation system that simulates the behaviour of a human translator through man−machine interaction. (2) Translation procedure A comparison of the procedures of human translation with those of computer-aided translation shows that the latter simulates the former in a number of ways. In manual translation, various translation procedures have been proposed by translation scholars and practitioners, ranging from the two-stage models to eight-stage ones, depending on the text type and purposes of translation. In machine translation and computer-aided translation, the process is known as technology-oriented translation procedure. (a) Two-stage model In human translation, the first type of translation procedure is a two-stage one, which includes the stage of source text comprehension and the stage of target text formulation, as shown below: Figure 2.1 A two-stage model for human translation Figure 2.1 is a model for human translators with the ability of comprehension. As a computer- aided translation system does not have the ability of comprehension, it cannot model after human translation with this two-stage model. It can, however, work on a two-stage translation with the use of its system dictionary, particularly in the case of a language-pair-specific system, as in Figure 2.2: Figure 2.2 A two-stage dictionary-based language-pair-specific model 34 Another two-stage model of computer-aided translation is a terminology-based system, as shown in Figure 2.3: Figure 2.3 A two-stage terminology-based CAT system (b) Three-stage models The second type of translation procedure is a three-stage model. This section covers five variations of this model proposed by Eugene Nida and Charles Taber (1969), Wolfram Wilss (1982), Roger Bell (1991), Basil Hatim and Ian Mason, and Jean Delisle (1988) respectively. A three-stage example-based computer-aided translation system is shown to illustrate the simulation of human translation by computer-aided translation. (i) model by eugene nida and charles taber The first model of a three-stage translation procedure involving the three phases of analysis, transfer, and restructuring was proposed by Eugene Nida and Charles Taber ([1969] 1982: 104). They intended to apply elements of Chomsky’s transformational grammar to provide Bible translators with some guidelines when they translate ancient source texts into modern target texts, which are drastically different in languages and structures. Nida and Taber describe this three-stage model as a translation procedure in which the translator first analyses the message of the source language into its simplest and structurally clearest forms, transfers it at this level, and then restructures it to the level in the receptor language which is most appropriate for the audience which he intends to reach. (Nida and Taber [1969] 1982: 484) Analysis is described by these two scholars as ‘the set of procedures, including back transformation and componential analysis, which aim at discovering the kernels underlying the source text and the clearest understanding of the meaning, in preparation for the transfer’ (Nida and Taber [1969] 1982: 197). Transfer, on the other hand, is described as the second stage ‘in which the analysed material is transferred in the mind of the translator from language A to language B’ (ibid.: 104). Restructuring is the final stage in which the results of the transfer process are transformed into a ‘stylistic form appropriate to the receptor language and to the intended receptors’. S. Chan Computer-aided translation35 In short, analysis, the first stage, is to analyse the source text, transfer, the second stage, is to transfer the meaning, and restructuring, the final stage, is to produce the target text. Their model is shown in Figure 2.4. Figure 2.4 Three-stage model by Nida and Taber (1964) (ii) model by wolfram wilss The second three-stage model was proposed by Wolfram Wilss (1982) who regards translation procedure as a linguistic process of decoding, transfer and encoding. His model is shown in Figure 2.5. Figure 2.5 Three-stage model by Wolfram Wilss (1982) (iii) model by roger bell Another three-stage model of note is by Roger Bell whose translation procedure framework is divided into three phases: the first phase is source text interpretation and analysis, the second, translation process, and the third, text reformulation (see Figure 2.6). The last phase takes into consideration three factors: writer’s intention, reader’s expectation, and the target language norms (Bell 1991). 36 Figure 2.6 Model of Roger Bell (iv) model by basil hatim and ian mason This model, proposed by Basil Hatim and Ian Mason, is a more sophisticated three-stage model, which involves the three steps of source text comprehension, transfer of meaning, and target text assessment (see Figure 2.7). At the source text comprehension level, text parsing, specialized knowledge, and intended meaning are examined. At the meaning transfer stage, consideration is given to the lexical meaning, grammatical meaning, and rhetorical meaning. At the target text assessment level, attention is paid to text readability, target language conventions, and the adequacy of purpose. Figure 2.7 A three-stage model by Basil Hatim and Ian Mason (v) model of jean delisle The fourth model of a three-stage translation procedure was proposed by Jean Delisle (1988: 53−69) (see Figure 2.8). Deslisle believes that there are three stages in the development of a translation equivalence: comprehension, reformulation, and verification: ‘comprehension is S. Chan Computer-aided translation37 based on decoding linguistic signs and grasping meaning, reformulation is a matter of reasoning by analogy and re-wording concepts, and verification involves back-interpreting and choosing a solution’ (1988: 53).Parallel to human translation, a three-stage model in computer-aided translation is the example-based system. The input text goes through the translation memory database and glossary database to generate fuzzy matches and translations of terms before getting the target text. The procedure of an example-based computer-aided translation system is shown in Figure 2.9. Figure 2.8 A three-stage model of Jean Delisle Figure 2.9 Three-stage example-based computer-aided translation model (c) Four-stage model The third type of translation procedure is a four-stage one. A typical example is given by George Steiner ([1975] 1992) who believes that the four stages of translation procedure are: knowledge of the author’s times, familiarization with author’s sphere of sensibility, original text decoding, and target text encoding (see Figure 2.10). 38 Figure 2.10 Model of George Steiner (1975) For computer-aided translation, a four-stage model is exemplified by webpage translation provided by Yaxin. The first stage is to input the Chinese webpage, the second stage is to process the webpage with the multilingual maintenance platform, the third stage is to process it with the terminology database, and the final stage is to generate a bilingual webpage. The Yaxin translation procedure is shown in Figure 2.11. Figure 2.11 Yaxin’s four-stage procedure (d) Five-stage model The fourth type of translation procedure is a five-stage one, as proposed by Omar Sheikh Al- Shabab (1996: 52) (see Figure 2.12). The first stage is to edit the source text, the second, interpret the source text, the third, interpret it in a new language, the fourth, formulate the translated text, and the fifth, edit the formulation.In computer-aided translation, a five-stage model is normally practised. At the first stage, the Initiating Stage, tasks such as setting computer specifications, logging in a system, creating a profile, and creating a project file are performed. At the second stage, the Data Preparation Stage, the tasks involve data collection, data creation, and the creation of terminology and translation memory databases. At the third stage, the Data Processing Stage, the tasks include data analysis, the use of system and non-system dictionaries, the use of concordancers, doing S. Chan Computer-aided translation39 Figure 2.12 Model of Omar Sheikh Al-Shabab pre-translation, data processing by translation by computer-aided translation systems with human intervention, or by machine translation systems without human intervention, or data processing by localization systems. At the fourth stage, the Data Editing Stage, the work is divided into two types. One type is data editing for computer-aided translation systems, which is about interactive editing, the editing environments, matching, and methods used in computer-aided translation. Another type is data editing for computer translation systems, which is about post-editing and the methods used in human translation. At the last or fifth stage, the Finalizing Stage, the work is mainly updating databases. The fives stages in computer-aided translation are illustrated in Figure 2.13. Figure 2.13 Five-stage technology-oriented translation procedure model It can be seen that though there are both five-stage models in human translation and computer- aided translation and the tasks involved are different, the concept of simulativity is at work at almost all stages. (e) Eight-stage model The fifth type of translation procedure is an eight-stage one, as proposed by Robert Bly (1983). 40 Robert Bly, who is a poet, suggests an eight-stage procedure for the translation of poetry: (a) set down a literal version; (b) find out the meaning of the poem; (c) make it sound like English; (d) make it sound like American; (e) catch the mood of the poem; (f) pay attention to sound; (g) ask a native speaker to go over the version; and (h) make a final draft with some adjustments (see Figure 2.14). Figure 2.14 Model by Robert Bly (1983) In computer-aided translation, there is no eight-stage model. But other than the five-stage model, there is also a seven-stage model, which is shown in Figure 2.15. Figure 2.15 Seven-stage computer-aided translation procedure The seven stages of computer-aided translation go from sample text collection to termbase creation, translation memory database creation, source text selection, data retrieval, source text translation and finally data updating. S. Chan Computer-aided translation41 All in all, we can say that when compared to human translation, computer-aided translation is simulative, following some of the stages in human translation. Emulativity There are obviously some functions which are performable by a computer-aided translation system, but not by a human translation. This is how technology can emulate human translation. Computer-aided translation, with the help of machine translation, simulates human translation, and it also emulates human translation in a number of areas of computer- aided translation, some of which are mentioned below. Alt-tag translation This function of machine translation allows the user to understand the meaning of text embedded within images (Joy 2002). The images on a web site are created by IMG tag (inline image graphic tag), and the text that provides an alternative message to viewers who cannot see the graphics is known as ALT-tag, which stands for ‘alternative text’. Adding an appropriate ALT-tag to every image within one’s web site will make a huge difference to its accessibility. As translators, our concern is the translation of the alternative text, as images are not to be translated anyway. Chatroom translation Machine translation has the function to translate the contents of a chatroom, known as ‘chat translation’ or ‘chatroom translation’. Chat translation systems are commercially available for the translation of the contents of the Chatroom on the computer. As a chat is part of conversational discourse, all the theoretical and practical issues relating to conversational discourse can be applied to the study of chat translation. It should be noted that this kind of online jargon and addressivity are drastically different from what we have in other modes of communication. The function of Chatroom is available in some systems, such as Fluency, as one of the resources. This function has to be purchased and enabled in the Fluency Chat Server to allow clients to be connected to this closed system for internal communications. For standalone version users, the function of Chat will be provided by Fluency Chat Server provided by its company, the Western Standard (Western Standard 2011: 39). Clipboard translation This is to copy a text to the clipboard from any Windows application for a machine translation system to translate the clipboard text, and the translated text can then be pasted in the original or any other location. One of the systems that translate clipboards is Atlas. Conversion between metric and British systems A function that can be easily handled by machine translation but not so easily by human translation is the conversion of weight, volume, length, or temperature from metric to British or vice versa. Fluency, for example, can do the metric/British conversion the target text box with the converted units. 42 Currency conversion Some computer-aided translation systems can do currency conversion. With the use of Currency Converter, a function in Fluency, and access to the Internet to get the currency conversion rates, systems can convert a currency in a country into the local country currency. The number of currencies that can be handled by a system is relatively large. Fluency, for example, supports the conversion of currencies of around 220 countries. The conversion of multiple currencies is also supported. Email translation This refers to the translation of emails by a machine translation system (Matsuda and Kumai 1999; Rooke 1985: 105−115). The first online and real-time email translation was made in 1994 by the CompuServe service which provided translation service of emails from and to English and French, German or Spanish. Email translation has since become a very important part of daily communication and most web translation tools have email translators to translate emails. As emails are usually conversational and often written in an informal or even ungrammatical way, they are difficult for mechanical processing (Fais and Ogura 2001; Han, Gates and Levin 2006). One of the systems that translates emails is Atlas. Foreign language translation One of the most important purposes of using translation software is to translate a source text the language of which is unfamiliar to the user so as to explain its contents in a language familiar to the user. It is found that a majority of the commercial machine translation systems are for translation among Indo-European languages or major languages with a large number of speakers or users. Software for translation between major languages and minor languages are relatively small in number. Gist translation Another area where machine translation differs fundamentally from human translation is gist translation, which refers to a translation output which expresses only a condensed version of the source text message. This type of rough translation is to get some essential information about what is in the text for a user to decide whether to translate it in full or not to serve some specific purposes. Highlight and translate This function allows the user to highlight a part of the text and translate it into the designated language. The highlighted text is translated on its own without affecting the rest of the text. Instant transliteration This refers to a function of machine translation which can transliterate the words of a text with a certain romanization system. In the case of Chinese, the Hanyu Pinyin Romanization system for simplified characters is used in mainland China, while the Wade-Giles Romanization system for traditional characters is used in Taiwan. S. Chan Computer-aided translation43 Mouse translation This is to translate sentences on a web page or on applications by simply clicking the mouse. Systems that provide mouse translation include Atlas. Online translation This is the translation of a text by an online machine translation system which is available at all times on demand from users. With the use of online translation service, the functions of information assimilation, message dissemination, language communication, translation entertainment, and language learning can be achieved. Pre-translation Machine translation is taken to be pre-translation in two respects. The first is as a kind of preparatory work on the texts to be translated, including the checking of spelling, the compilation of dictionaries, and the adjustment of text format. The second is taken to be a draft translation of the source text which can be further revised by a human translator. Sentence translation Unlike human translation which works at the textual level, machine translation is sentential translation. In other words, machine translation is a sentence-by-sentence translation. This type of translation facilitates the work of post-editing and methods which are frequently used in translating sentences in translation practice to produce effective translations can be used to produce good translations from machine translation systems. Web translation This refers to the translation of information on a web page from one language into another. Web-translation tools are a type of translation tools which translate information on a web page from one language into another. They serve three functions: (1) as an assimilation tool to transmit information to the user; (2) as a dissemination tool to make messages comprehensible; and (3) as a communication tool to enable communication between people with different language backgrounds. Productivity As translation technology is a field of entrepreneurial humanities, productivity is of great importance. Productivity in computer-aided translation is achieved through the use of technology, collective translation, recycling translations, reusing translations, professional competence, profit-seeking, labour-saving, and cost-saving. Using technology to increase productivity The use of technology to increase productivity needs no explanation. As early as 1980, when Martin Kay discussed the proper place of men and machines in language translation, he said: 44 Translation is a fine and exacting art, but there is much about it that is mechanical and routine and, if this were given over to a machine, the productivity of the translator would not only be magnified but his work would become more rewarding, more exciting, more human.(Kay 1980: 1) All computer-aided translation systems aim to increase translation productivity. In terms of the means of production, all translation nowadays is computer-aided translation as virtually no one could translate without using a computer. Collective translation to increase productivity Gone are the days when bilingual competence, pen and paper, and printed dictionaries made a translator. Gone are the days when a single translator did a long translation project all by himself. It is true that in the past, translation was mainly done singly and individually. Translation was also done in a leisurely manner. At present, translation is done largely through team work linked by a server-based computer-aided translation system. In other words, translation is done in a collective manner. Recycling translations to increase productivity To recycle a translation in computer-aided translation is to use exact matches automatically extracted from a translation memory database. To increase productivity, the practice of recycling translations is followed in computer-aided translation. Networked computer-aided translation systems are used to store centralized translation data, which are created by and distributed among translators. As this is the case, translators do not have to produce their own translations. They can simply draw from and make use of the translations stored in the bilingual database to form their translation of the source text. Translation is therefore produced by selection. Reusing translations to increase productivity To reuse a translation in computer-aided translation is to appropriate terms and expressions stored in a term database and translation memory database. It should be noted that while in literary translation, translators produce translations in a creative manner, translators in practical translation reuse and recycle translations as the original texts are often repetitive. In the present age, over 90 per cent of translation work is in the area of practical translation. Computer-aided translation is ideal for the translation of repetitive practical writings. Translators do not have to translate the sentences they have translated before. The more they translate, the less they have to translate. Computer-aided translation therefore reduces the amount a translator needs to translate by eliminating duplicate work. Some systems, such as Across, allow the user to automatically reuse existing translations from the trans lation memory. It can be seen that ‘reduce, reuse, recycle’ are the three effective ways of increasing profitability (de Ilarraza, Mayor and Sarasola 2000). S. Chan Computer-aided translation45 Professional competence to increase productivity Translators have to work with the help of translation technology. The use of computer-aided translation tools has actually been extended to almost every type of translation work. Computer- aided translation tools are aimed at supporting translators and not at replacing them. They make sure that translation quality is maintained as ‘all output is human input’. As far as the use of tools is concerned, professional translation is technological. In the past, translators used only printed dictionaries and references. Nowadays, translators use electronic concordancers, speech technology, online terminology systems, and automatic checkers. Translation is about the use of a workbench or workstation in translation work. Translation competence or knowledge and skills in languages are not enough today. It is more realistic to talk about professional competence, which includes linguistic competence, cultural competence, translation competence, translator competence, and technological competence. Professional competence is important for translators as it affects their career development. A remark made by Timothy Hunt is worth noting: ‘Computers will never replace translators, but translators who use computers will replace translators who don’t’ (Sofer 2009: 88). What has happened in the field of translation technology shows that Hunt’s remark may not be far off the mark. In the 1980s, very few people had any ideas about translation technology or computer-aided translation. Now, SDL alone has more than 180,000 computer- aided translators. The total number of computer-aided translators in the world is likely to be several times higher than the SDL translators. Profit-seeking to increase productivity Translation is in part vocational, in part academic. In the training of translators, there are courses on translation skills to foster their professionalism, and there are courses on translation theories to enhance their academic knowledge. But there are very few courses on translation as a business or as an industry. It should be noted that translation in recent decades has increasingly become a field of entrepreneurial humanities as a result of the creation of the project management function in computer-aided translation systems. This means translation is now a field of humanities which is entrepreneurial in nature. Translation as a commercial activity has to increase productivity to make more profits. Labour-saving to increase productivity Computer-aided translation systems help to increase productivity and profits through labour- saving, eliminating repetitive translation tasks. Through reusing past translations, an enormous amount of labour is saved. Computer-aided translation tools support translators by freeing them from boring work and letting them concentrate on what they can do best over machines, i.e. handling semantics and pragmatics. Generally, this leads to a broader acceptance by translators. The role of a translator, therefore, has changed drastically in the modern age of digital communication. Rather than simply translating the document, a computer-aided translator has to engage in other types of work, such as authoring, pre-editing, interactive editing, post-editing, term database management, translation memory database management, text alignment and manual alignment verification. It is estimated that with the use of translation technology, the work that was originally borne by six translators can be taken up by just one. 46 Cost-saving to increase productivity Computer-aided translation is also cost-saving. It helps to keep the overhead cost down as what has been translated needs not to be translated again. It helps to improve budget planning. Other issues relating to cost should also be taken into account. First, the actual cost of the tool and its periodic upgrades. Second, the licensing policy of the system, which is about the ease of transferring licences between computers or servers, the incurring of extra charges for client licences, the lending of licences to one’s vendors, freelances, and the eligibility for free upgrades. Third, the cost that is required for support, maintenance, or training. Fourth, the affordability of the system for one’s translators. Fifth, the user-friendliness of the system to one’s computer technicians and translators, which affects the cost of production. Compatibility The concept of compatibility in translation technology must be considered in terms of file formats, operating systems, intersystem formats, translation memory databases, terminology databases, and the languages supported by different systems. Compatibility of file formats One of the most important concepts in translation technology is the type of data that needs to be processed, which is indicated by its format, being shown by one or several letters at the end of a filename. Filename extensions usually follow a period (dot) and indicate the type of information stored in the file. A look at some of the common file types and their file extensions shows that in translation technology, text translation is but one type of data processing, though it is the most popular one. There are two major types of formats: general documentation types and software development types. (I) General documentation types (1) Text files All computer-aided translation systems which use Microsoft Word as text editor can process all formats recognized by Microsoft Word. Throughout the development of translation technology, most computer-aided translation systems process text files (.txt). For Microsoft Word 2000−2003, text files were saved and stored as .doc (document text file/word processing file); for Microsoft Word 2007−2011, documents were saved and stored as .docx (Document text file (Microsoft Office 2007)), .dotx (Microsoft Word 2007 Document Template). Other types of text files include .txt (Text files), .txml (WordFast files), and .rtf (Rich Text files). All automatic and interactive translation systems can process text files, provided the text processing system has been installed in the computer before processing begins. Some of the computer-aided translation systems which can only translate text files include: Across, AidTransStudio, Anaphraseus (formerly known as OpenWordfast), AnyMem (.docx or higher), Araya, Autshumato Integrated Translation Environment (ITE), CafeTran, Déjà Vu, Esperantilo, Fluency, Fusion, OmegaT, Wordfast, and WordFisher. Computer-aided translation systems which can translate text files as well as other formats include CafeTran, Esperantilo, Felix, Fortis, GlobalSight, Google Translator Toolkit, Heartsome Translation Suite, Huajian IAT, Lingo, Lingotek, MadCap Lingo, MemoQ, MemOrg, MemSource, MetaTexis, MultiTrans, S. Chan Computer-aided translation47 OmegaT+, Pootle, SDL-Trados, Similis, Snowman, Swordfish, TM-database, Transit, Wordfast, XTM, and Yaxin. (2) Web-page files HyperText Markup Language (HTML) is a markup language that web browsers use to interpret and compose text, images and other material into visual or audible web pages. HTML defines the structure and layout of a web page or document by using a variety of tags and attributes. HTML documents are stored as .asp (Active Server Pages), .aspx (Active Server Page Extended), .htm (Hypertext Markup Language), .html (Hypertext Markup Language Files), .php (originally: Personal Home Page; now: Hypertext Preprocessor), .jsp (JavaServer Pages), .sgml (Standard Generalized Markup Language File), .xml (Extensible Markup Language file), .xsl (Extensible Stylesheet Language file) files format, which were available since late 1991. Due to the popularity of web pages, web translation has been an important part of automatic and interactive translation systems. Many systems provide comprehensive support for the localization of HTML-based document types. Web page localization is interchangeable with web translation or web localization. Systems that handle HTML include Across, AidTransStudio, Alchemy Publisher, Araya, Atlas, CafeTran, CatsCradle, Déjà Vu, Felix, Fluency, Fortis, GlobalSight, Google Translator Toolkit, Heartsome Translation Suite, Huajian IAT, Lingo, Lingotek, LogiTerm, MemoQ, MemOrg, MetaTexis, MultiTrans, Okapi Framework, OmegaT, OmegaT+, Open Language Tools, Pootle, SDL-Trados, Similis, Snowman, Swordfish, TM-database, TransSearch, Transit, Transolution, and XTM. (3) PDF files Portable Document Format (PDF) (.pdf ) is a universally accepted file interchange format developed by Adobe in the 1990s. The software that allows document files to be transferred between different types of computers is Adobe Acrobat. A PDF file can be opened by the document format, which might require editing to make the file look more like the original, or can be converted to an rtf file for data processing by a computer-aided translation system. Systems that can translate Adobe PDF files and save them as Microsoft Word documents include Alchemy Publisher, CafeTran, Lingo, Similis, and Snowman. (4) Microsoft Office PowerPoint files Microsoft PowerPoint is a presentation program developed to enable users to create anything from basic slide shows to complex presentations, which are comprised of slides that may contain text, images, and other media. Versions of Microsoft Office PowerPoint include Microsoft PowerPoint 2000–2003, .ppt (General file extension), .pps (PowerPoint Slideshow), .pot (PowerPoint template); Microsoft PowerPoint 2007/2011, which are saved as .pptx (Microsoft PowerPoint Open XML Document), .ppsx (PowerPoint Open XML Slide Show), .potx (PowerPoint Open XML Presentation Template), and .ppsm (PowerPoint 2007 Macro- enabled Slide Show). Systems that can handle Powerpoint files include Across, AidTransStudio, Alchemy Publisher, CafeTran, Déjà Vu, Felix, Fluency, Fusion, GlobalSight, Lingotek, LogiTerm, MadCap Lingo, MemoQ, MemSource, MetaTexis, SDL-Trados, Swordfish, TM-database, Transit, Wordfast, XTM, and Yaxin. 48 (5) Microsoft Excel files Different versions of Microsoft Excel include Microsoft Excel 2000–2003 .xls (spreadsheet), .xlt (template); Microsoft Excel 2007: .xlsx (Microsoft Excel Open XML Document), .xltx (Excel 2007 spreadsheet template), .xlsm (Excel 2007 macro-enabled spreadsheet) The computer-aided translation systems that can translate Excel files include Across, AidTransStudio, Déjà Vu, Felix, GlobalSight, Lingotek, LogiTerm, and MemoQ, MemOrg, MetaTexis, MultiTrans, Snowman, Wordfast, and Yaxin. (6) Microsoft Access files One of the computer-aided translation systems which can handle Access with .accdb (Access 2007–2010) file extension is Déjà Vu. (7) Image files The processing of image data, mainly graphics and pictures, is important in computer-aided translation. The data is stored as .bmp (bitmap image file), .jpg (Joint Photographic Experts Group), and .gif (Graphics Interchange Format). One of the computer-aided translation systems that is capable of handling images is CafeTran. (8) Subtitle files One of the most popular subtitle files on the market is .srt (SubRip Text). OmegaT is one of the computer-aided systems that supports subtitle files. (9) Adobe InDesign files Adobe InDesign is desktop publishing software. It can be translated without the need of any third party software by Alchemy Publisher and AnyMem. For Alchemy Publisher, the .indd file must be exported to an .inx format before it can be processed. Other computer-aided translation systems that support Adobe Indesign files include Across, Déjà Vu, Fortis, GlobalSight, Heartsome Translation Suite, Okapi Framework, MemoQ, MultiTrans, SDL-Trados, Swordfish, Transit, and XTM. (10) Adobe FrameMaker Files Adobe FrameMaker is an authoring and publishing solution for XML. FrameMaker files, .fm, .mif and .book, can be opened directly by a system if it is installed with Adobe FrameMaker. Computer-aided translation systems that can translate Adobe FrameMaker files include Across, Alchemy Publisher (which requires a PPF created by Adobe FrameMaker before translating it. Alchemy Publisher supports FrameMaker 5.0, 6.0, 7.0, 8.0, 9.0, FrameBuilder 4.0, and FrameMaker + sgml), CafeTran, Déjà Vu, Fortis, GlobalSight, Heartsome Translation Suite, Lingo, Lingotek, MadCap Lingo, MemoQ, MetaTexis, MultiTrans, SDL-Trados, Swordfish, Transit, Wordfast, and XTM. (11) Adobe PageMaker files Systems that support Adobe PageMaker 6.5 and 7 files include Déjà Vu, GlobalSight, MetaTexis, and Transit. (12) AutoCAD files AutoCAD, developed and first released by Autodesk, Inc. in December 1982, is a software application for computer-aided design (CAD) and drafting which supports both 2D and 3D S. Chan Computer-aided translation49 formats. This software is now used internationally as the most popular drafting tool for a range of industries, most commonly in architecture and engineering. Computer-aided translation systems that support AutoCad are CafeTran, Transit, and TranslateCAD. (13) DTP tagged text files DTP stands for Desktop Publishing. A popular desktop publishing system is QuarkXPress. Systems that support desktop publishing include Across, CafeTran, Déjà Vu, Fortis, GlobalSight, MetaTexis, MultiTrans, SDL-Trados, and Transit. (14) Localization files Localization files include files with the standardized format for localization .xliff (XML Localization Interchange File Format) files, .ttx (XML font file format) files, and .po (Portable Object). Computer-aided translation systems which process XLIFF files include Across Language Server, Araya, CafeTran, Esperantilo, Fluency, Fortis, GTranslator, Heartsome Translation Suite, MadCap Lingo, Lingotek, MemoQ, Okapi Framework, Open Language Tools, Poedit, Pootle, Swordfish, Transolution, Virtaal, and XTM. (II) Software development types (1) Java Properties files Java Properties files are simple text files that are used in Java applications. The file extension of Java Properties file is .properties. Computer-aided translation systems that support Java Properties File include Déjà Vu, Fortis, Heartsome Translation Suite, Lingotek, Okapi Framework, OmegaT+, Open Language Tools, Pootle, Swordfish, and XTM. (2) OpenOffice.org/StarOffice StarOffice of the Star Division was a German company that ran from 1984 to 1999. It was succeeded by OpenOffice.org, an open-sourced version of StarOffice owned by Sun Microsystems (1999–2009) and by Oracle Corporation (2010–2011), which ran from 1999−2011.Currently it is Apache OpenOffice. The format of OpenOffice is .odf (Open Document Format). Computer-aided translation systems which process this type of file include AidTransStudio, Anaphraseus, CafeTran, Déjà Vu, Heartsome Translation Suite, Lingotek, OmegaT, OmegaT+, Open Language Tools, Pootle, Similis, Swordfish, Transolution, and XTM. (3) Windows resource files These are simple script files containing startup instructions for an application program, usually a text file containing commands that are compiled into binary files such as .exe and .dll. File extensions include .rc (Record Columnar File), .resx (NET XML Resource Template). Computer-aided translation systems that process this type of files include Across, Déjà Vu, Fortis, Lingotek, MetaTexis, and Okapi Framework. 50 Compatibility of operating systems One of the most important factors which determined the course of development of computer- aided translation systems is their compatibility with the current operating systems on the market. It is therefore essential to examine the major operating systems running from the beginning of computer-aided translation in 1988 to the present, which include, among others, the Windows of Microsoft and the OS of Macintosh. Microsoft Operating Systems In the world of computing, Microsoft Windows has been the dominant operating system. From the 1981 to the 1995, the x86-based MS-DOS (Microsoft Disk Operating System) was the most commonly used system, especially for IBM PC compatible personal computers. Trados’s Translator’s Workbench II, developed in 1992, is a typical example of a computer- aided translation system working on DOS.DOS was supplemented by Microsoft Windows 1.0, a 16-bit graphical operating environment, released on 20 November 1985 (Windows 2012). In November 1987, Windows 1.0 was succeeded by Windows 2.0, which existed till 2001. Déjà Vu 1.0, released in 1993, was one of the systems compatible with Windows 2.0. Windows 2.0 was supplemented by Windows 286 and Windows 386. Then came Windows 3.0, succeeding Windows 2.1x. Windows 3.0, with a graphical environment, is the third major release of Microsoft Windows, and was released on 22 May 1990. With a significantly revamped user interface and technical improvements, Windows 3 became the first widely successful version of Windows and a rival to Apple Macintosh and the Commodore Amiga on the GUI front. It was followed by Windows 3.1x. During its lifespan from 1992−2001, Windows 3.1x introduced various enhancements to the still MS-DOS- based platform, including improved system stability, expanded support for multimedia, TrueType fonts, and workgroup networking. Trados’s Translator’s Workbench, released in 1994, was a system that was adaptable to Windows 3.1x. Except for Windows and DOS, OS/2 is also one of the operation systems that support computer-aided translation systems, especially in late 1980s and early 1990s. Apple Operating Systems Mac OS (1984−2000) and OS X (2001−) are two series of graphical user interface-based operating systems developed by Apple Inc. for their Macintosh line of computer systems. Mac OS was first introduced in 1984 with the original Macintosh and this series was ended in 2000. OS X, first released in March 2001, is a series of Unix-based graphical interface operating systems. Both series share a general interface design, but have very different internal architectures. Only one computer-aided translation system, AppleTrans, is designed for OS X. Its initial released was announced in February 2004 and the latest updated version was version 1.2(v38) released in September 2006, which runs on Mac OS X 10.3 or later. Another computer-aided translation system, Wordfast Classic was released to upgrade its support of the latest text processor running on Mac OS X, such as Wordfast Classic 6.0, which is compatible for MS Word 2011 for Mac. Other computer-aided translation systems that can run on Mac OS or OS X are cross- platform software, rather than software developed particularly for Mac. Examples are Java- based applications, such as Autshumato, Heartsome, OmegaT, Open Language Tools and S. Chan Computer-aided translation51 Swordfish. Besides, all cloud-based systems can support Mac OS and OS X, including Wordbee, XTM Cloud, Google Translator’s Toolkit, Lingotek Collaborative Translation Platform, MemSource Cloud, and WebWordSystem.OS/2 is a series of computer operating systems, initially created by Microsoft and IBM, then later developed by IBM exclusively. The name stands for ‘Operating System/2’. Until 1992, the early computer-aided translation systems ran either on MS-DOS or OS/2. For example, IBM Translation Manager/2 (TM/2) was released in 1992 and run on OS/2. ALPS’s translation tool also ran on OS/2. But OS/2 had a much smaller market share compared with Windows in early 1990s. Computer-aided translation system developers therefore gradually shifted from OS/2 and MS-DOS to Windows or discontinued the development of OS/2 and MS-DOS compatible computer-aided translation systems. By the end of the 1990s, most computer-aided translation systems mainly ran on Windows, although some developers offered operating-system customization services upon request. OS/2 4.52 was released in December 2001. IBM ended its support to OS/2 on 31 December 2006. Compatibility of databases Compatibility of translation memory databases TMX (Translation Memory eXchange), created in 1998, is widely used as an interchange format between different translation memory formats. TMX files are XML (eXtensible Markup Language) files whose format was originally developed and maintained by OSCAR (Open Standards for Container/Content Allowing Re-use) of the Localization Industry Standards Association. The latest official version of the TMX specification, version 1.4b, was released in 2005. In March 2011 LISA was declared insolvent; as a result its standards were moved under the Creative Commons licence and the standards specification relocated. The technical specification and a sample document of TMX can be found on the website of The Globalization and Localization Association. TMX has been widely adopted and is supported by more than half of the current computer- aided translation systems on the market. The total number of computer-aided translation systems that can import and export translation memories in TMX format is 54, including Across, Alchemy Publisher, Anaphraseus, AnyMem, Araya, ATLAS, Autshumato, CafeTran, Crowdin, Déjà Vu, EsperantiloTM, Felix, Fluency, Fortis, Fusion, GE-CCT, GlobalSight, Google Translator Toolkit, Heartsome, Huajian IAT, Lingotek, LogiTerm, LongRay CAT, MadCap Lingo, MemoQ, MemSource, MetaTexis, MT2007, MultiTrans, OmegaT, OmegaT+, Open Language Tools, OpenTM2, OpenTMS, PROMT, SDL Trados, Snowball, Snowman, Swordfish, Systran, Text United, The Hongyahu, TM Database, Transit, Translation Workspace, Transwhiz, TraTool, Webwordsystem, Wordbee Translator, Wordfast Classic and Wordfast Pro, XTM, Yaxin CAT, and 翻訳ブレイン (Translation Brain). Compatibility of terminology databases Compatibility of terminology databases is best illustrated by TermBase eXchange (TBX), which covers a family of formats for representing the information in a high-end termbase in a neutral intermediate format in a manner compliant with the Terminological Markup Framework (TMF) (Melby 2012: 19−21). Termbase Exchange is an international standard as well as an industry standard. The industry standard version differs from the ISO standard only by having different title pages. Localization 52 Industry Standards Association, the host organization for OSCAR that developed Termbase Exchange, was dissolved in February 2011. In September 2011, the European Telecommunications Standards Institute (ETSI) took over maintenance of the OSCAR standards. ETSI has established an interest group for translation/localization standards and a liaison relationship with the International Organization for Standardization (ISO) so that TBX can continue to be published as both an ISO standard and an industry standard. There are many types of termbases in use, ranging from huge termbases operated by governments, to medium-size termbases maintained by corporations and non-governmental organizations, to smaller termbases maintained by translation service providers and individual translators. The problem addressed by the designers of term exchange was that existing termbases are generally not interoperable. They are based on different data models that use a variety of data categories. And even if the same data category is used for a particular piece of information, the name of the data category and the values allowed for the data category may be different. Compatibility of rules segmentation rules exchange Segmentation Rules eXchange (SRX) is an XML-based standard that was maintained by Localization Industry Standards Association, until it became insolvent in 2011 and then this standard is now maintained by the Globalization and Localization Association (GALA). Segmentation Rules eXchange provides a common way to describe how to segment text for translation and other language-related processes. It was created when it was realized that translation memory exchange leverage is lower than expected in certain instances due to differences in how tools segment text. Segmentation Rules eXchange is intended to enhance the translation memory exchange so that translation memory data that is exchanged between applications can be used more effectively. Having the segmentation rules that were used when a translation memory was created will increase the leverage that can be achieved when deploying the translation memory data. Compatibility with the languages supported As computer-aided translation systems cannot identify languages, language compatibility is therefore an important concept in translation technology. There are a large number of languages and sub-languages in the world, totalling 6,912. But the number of major languages computers can process is relatively small. It is therefore important to know whether the languages that require machine processing are supported by a system or not. With the aid of unicode, most of the languages in the world are supported in popular computer-aided translation systems. Unicode is a computing industry standard for the consistent encoding, representation and handling of text expressed in most of the world’s writing systems. There are basically two major types of language and sub-language codes. Some systems, such as OmegaT and XTM, use letters for language codes (2 or 3 letters) and language-and-region codes (2+2 letters), which can be selected from a drop-down list. OmegaT follows the ISO 639 Code Tables in preparing its code list. French for example, is coded fr with the language- and region code for French (Canada) as fr-CA. The following is a list of languages supported by Wordfast Classics and XTM, two of the nine computer-aided translation systems chosen for analysis in this chapter. S. Chan Computer-aided translation53 wordfast classic Wordfast can be used to translate any of the languages supported by Microsoft Word. The number of languages supported by Microsoft is 91, with a number of sub-languages for some major languages. [Afro-Asiatic] Arabic (Algeria), Arabic (Bahrain), Arabic (Egypt), Arabic (Iraq), Arabic (Jordan), Arabic (Kuwait), Arabic (Lebanon), Arabic (Libya), Arabic (Morocco), Arabic (Oman), Arabic (Qatar), Arabic (Saudi Arabia), Arabic (Syria), Arabic (Tunisia), Arabic (U.A.E.), Arabic (Yemen), Hebrew, Maltese [Altaic] Azeri (Cyrillic), Azeri (Latin), Japanese, Korean, Turkish [Austro-Asiatic] Vietnamese [Austronesian] Indonesian, Malay (Brunei Darussalam), Malaysian [Basque] Basque [Dravidian] Kannada, Malayalam, Tamil, Telugu [Indo-European] Afrikaans, Albanian, Armenian, Assamese, Belarusian, Bengali, Bulgarian, Byelorussian, Catalan, Croatian, Czech, Danish, Dutch, Dutch (Belgian), English (Australia), English (Belize), English (Canadian), English (Caribbean), English (Ireland), English (Jamaica), English (New Zealand), English (Philippines), English (South Africa), English (Trinidad), English (U.K.), English (U.S.), English (Zimbabwe), Faroese, Farsi, French (Belgian), French (Cameroon), French (Canadian), French (Congo), French (Cote d’Ivoire), French (Luxembourg), French (Mali), French (Monaco), French (Reunion), French (Senegal), French (West Indies), Frisian (Netherlands), Gaelic (Ireland), Gaelic (Scotland), Galician, German, German (Austria), German (Liechtenstein), German (Luxembourg), Greek, Gujarati, Hindi, Icelandic, Italian, Kashmiri, Konkani, Latvian, Lithuanian, Macedonian (FYRO), Marathi, Nepali, Norwegian (Bokmol), Norwegian (Nynorsk), Oriya, Polish, Portuguese, Portuguese (Brazil), Punjabi, Rhaeto-Romance, Romanian, Romanian (Moldova), Russian, Russian (Moldova), Sanskrit, Serbian (Cyrillic), Serbian (Latin), Sindhi, Slovak, Slovenian, Sorbian, Spanish (Argentina), Spanish (Bolivia), Spanish (Chile), Spanish (Colombia), Spanish (Costa Rica), Spanish (Dominican Republic), Spanish (Ecuador), Spanish (El Salvador), Spanish (Guatemala), Spanish (Honduras), Spanish (Nicaragua), Spanish (Panama), Spanish (Paraguay), Spanish (Peru), Spanish (Puerto Rico), Spanish (Spain), Spanish (Traditional), Spanish (Uruguay), Spanish (Venezuela), Swedish, Swedish (Finland), Swiss (French), Swiss (German), Swiss (Italian), Tajik, Ukrainian, Urdu, Welsh [Kartvelian] Georgian [Niger-Congo] Sesotho, Swahili, Tsonga, Tswana, Venda, Xhosa, Zulu [Sino-Tibetan] Burmese, Chinese, Chinese (Hong Kong SAR), Chinese (Macau SAR), Chinese (Simplified), Chinese (Singapore), Chinese (Traditional), Manipuri, Tibetan 54 [Tai-Kadai] Laothian, Thai [Turkic] Tatar, Turkmen, Uzbek (Cyrillic), Uzbek (Latin) [Uralic] Estonian, Finnish, Hungarian, Sami Lappish xtm The languages available in XTM are 157, not including varieties within a single language. These languages are as follows: [Afro-Asiatic] Afar, Amharic, Arabic, Hausa, Hebrew, Maltese, Oromo, Somali, Sudanese Arabic, Syriac, Tigrinya, [Altaic] Azeri, Japanese, Kazakh, Korean, Mongolian, Turkish [Austro-Asiatic] Khmer, Vietnamese [Austronesian] Fijian, Indonesian, Javanese, Malagasy, Malay, Maori, Nauru, Samoan, Tagalog, Tetum, Tonga [Aymaran] Aymara [Bantu] Kikongo [Basque] Basque [Constructed Language] Esperanto, Interlingua, Volapk [Dravidian] Kannada, Malayalam, Tamil, Telugu [English Creole] Bislama [Eskimo-Aleut] Greenlandic, Inuktitut, Inupiak [French Creole] Haitian Creole [Hmong-Mien] Hmong [Indo-European] Afrikaans, Armenian, Assamese, Asturian, Bengali, Bihari, Bosnian, Breton, Bulgarian, Byelorussian, Catalan, Corsican, Croatian, Czech, Danish, Dari, Dhivehi, Dutch, English, Faroese, Flemish, French, Frisian, Galician, German, Greek, Gujarati, Hindi, Icelandic, Irish, Italian, Kashmiri, Konkani, Kurdish, Latin, Latvian, Lithuanian, Macedonian, Marathi, Montenegrin, Nepali, Norwegian, Occitan, Oriya, Pashto, Persian, Polish, Portuguese, Punjabi, Rhaeto-Romance, Romanian, Russian, Sanskrit, Sardinian, Scottish Gaelic, Serbian, Sindhi, Singhalese, Slovak, Slovenian, Sorbian, Spanish, Swedish, Tajik, Ukrainian, Urdu, Welsh, Yiddish S. Chan Computer-aided translation55 [Kartvelian] Georgian [Ngbandi-based Creole] Sango [Niger-Congo] Chichewa, Fula, Igbo, Kinyarwanda, Kirundi, Kiswahili, Lingala, Ndebele, Northern Sotho, Sesotho, Setswana, Shona, Siswati, Tsonga, Tswana, Twi, Wolof, Xhosa, Yoruba, Zulu [Northwest Caucasian] Abkhazian [Quechuan] Quechua [Romanian] Moldavian [Sino-Tibetan] Bhutani, Burmese; Chinese, Tibetan [Tai-Kadai] Laothian, Thai [Tupi] Guarani [Turkic] Bashkir, Kirghiz, Tarar, Turkmen, Uyghur, Uzbek [Uralic] Estonian, Finnish, Hungarian Several observations can be made from the languages supported by the current eleven systems.(1) The number of languages supported by language-specific systems is small as they need to be supplied with language-specific dictionaries to function well. Yaxin is best for English− Chinese translation, covering two languages, while most non-language-specific systems support around or above 100 languages. (2) For the seven systems developed in Europe, the United Kingdom, and the United States, which include Across, Déjà Vu, MemoQ, OmegaT, SDL Trados, Wordfast, and XTM, the Indo-European languages take up around 51.89 per cent, while the proportion of the non- Indo-European languages is 48.11 per cent. Table 2.1 shows the details: Table 2.1 Statistics of languages supported by 7 CAT systems Name of the system Number of languages supported Number of language families supported Number and percentage of Indo-European languages Number and percentage of non-Indo-European languages Across 121 1861 (50.41%) 60 (49.59%) Déjà Vu 132 2166 (50%) 66 (50%) MemoQ 102 1654 (52.94%) 48 (47.06%) OmegaT 90 1448 (53.33%) 42 (46.67%) SDL Trados 115 1862 (53.91%) 53 (46.09%) Wordfast 91 1354 (59.34%) 37 (40.66%) XTM 157 2668 (43.31%) 89 (56.69%) 56 Controllability One of the main differences between human translation and computer-aided translation lies in the degree of control over the source text. In human translation, there is no need, or rather it is not the common practice, to control how and what the author should write. But in computer-aided translation, control over the input text may not be inappropriate as the output of an unedited or uncontrolled source language text is far from satisfactory (Adriaens and Macken 1995: 123−141; Allen and Hogan 2000: 62−71; Arnold et al. 1994; Hurst 1997: 59−70; Lehtola, Tenni and Bounsaythip 1998: 16−29; Mitamura 1999: 46−52; Murphy et al. 1998; Nyberg et al. 2003: 245−281; Ruffino 1985: 157−162). The concept of controllability is realized in computer-aided translation by the use of controlled language and the method of pre-editing. Controllability by the use of controlled language An effective means of achieving controllability in translation technology is controlled language (see Figure 2.16). The idea of controlled language was created, partly at least, as a result of the problems with natural languages which are full of complexities, ambiguities, and robustness (Nyberg et al. 2003: 245−281). A strong rationale for controlled language is that a varied source text generates a poor target text, while a controlled source text produces a quality target text. (Bernth 1999). Controlled language is therefore considered necessary (Caeyers 1997: 91−103; Hu 2005: 364−372). Controlled language, in brief, refers to a type of natural language developed for specific domains with a clearly defined restriction on controlled lexicons, simplified grammars, and style rules to reduce the ambiguity and complexity of a text so as to make it easier to be understood by users and non-native speakers and processed by machine translation systems (Chan 2004: 44; Lux and Dauphin 1996: 193−204). Control over the three stages of a translation procedure, which include the stage of inputting a source text, the stage of transfer, and the stage of text generation, is generally regarded as a safe guarantee of quality translation. Control of the source text is in the form of controlled authoring, which makes the source text easier for computer processing (Allen 1999; Chan 2004: 44; van der Eijk and van Wees 1998: 65−70; Zydron 2003). The text produced is a ‘controlled language text’ (Melby 1995: 1). There is also control over the transfer stage. And the output of a machine translation system is known as ‘controlled translation’ (Carl 2003: 16−24; Gough and Way 2004: 73−81; Rico and Torrejon 2004; Roturier 2004; Torrejón 2002: 107−116), which is alternatively known as a ‘controlled target language text’ (Chan 2004: 44). In short, a controlled text is easier to be processed by machine translation systems to produce a quality output. Goals and means of controlled language Controlled language is used by both humans and computers. The goals of controlled language are to make the source text easier to read and understand. These goals are to be achieved at the lexical and sentential levels. At the lexical level, controlled language is about the removal of lexical ambiguity and the reduction in homonymy, synonymy, and complexity. This is to be achieved by one-to-one correspondence in the use and translation of words, known as one-word one-meaning. An example is to use only the word ‘start’ but not similar words such as ‘begin’, ‘commence’, S. Chan Computer-aided translation57 Figure 2.16 Controlled language ‘initiate’, and ‘originate’. The second method is to use the preferred language, such as American English but not British English. The third method is to have a limited basic vocabulary (Bjarnestam 2003; Chen and Wu 1999; Probst and Levin 2002: 157−167; Wasson 2000: 276−281), which can be illustrated by the use of a controlled vocabulary of 3,100 words in aircraft-maintenance documentation at the European Association of Aerospace Industries (AECMA) in 1980 (AECMA 1995).At the sentential level, controlled language is about the removal of syntactical ambiguity, the simplification of sentence structures, limitations on sentence length, and constraints on voice, tense, and other grammatical units. To do all these, there are a limited number of strictly stipulated writing rules to follow. The European Association of Aerospace Industries had 57 writing rules. Short sentences are preferred over long and complex sentences. And there is also a limit on the number of words in a sentence. For procedural text, there should be no more than twenty words. For descriptive texts, the number is twenty-five. There are also rules governing grammatical well-formedness (Loong 1989: 281−297), restricted syntax, and the use of passive construction in procedural texts. At the suprasentential level, there is a limit of six sentences in a paragraph, the maximum number of clauses in a sentence, and the use of separate sentences for sequential steps in procedural texts. This means setting limits on the length of a sentence, such as setting the number of words at twenty, using only the active voice, and expressing one instruction or idea by one sentence. Controlled language checkers Controlled language cannot be maintained manually; it relies on the use of different kinds of checkers, which are systems to ensure that a text conforms to the rules of a particular controlled language (Fouvry and Balkan 1996: 179−192). There is the automatic rewriting system, which is specially developed for controlled language, rewriting texts automatically into controlled language without changing the meaning of the sentences in the original in order to produce a high-quality machine translation. There is the controlled language checker, which is software that helps an author to determine whether a text conforms to the approved words and writing rules of a particular controlled language. Checkers can also be divided into two types: in-house controlled language checker and commercial controlled language checker. In-house controlled language checkers include the 58 PACE (Perkins Approved Clear English) of Perkins Engines Ltd, the Controlled English of Alcatel Telecom, and the Boeing Simplified English Checker of the Boeing Company (Wojcik and Holmback 1996: 22−31). For commercial controlled language checkers, there are a number of popular systems. The LANTmaster Controlled Checker, for example, is a controlled language checker developed by LANT in Belgium. It is based on work done for the METAL (Mechanical Translation and Analysis of Languages) machine translation project. It is also based on the experience of the Simplified English Grammar and Style Checker (SECC) project (Adriaens 1994: 78–88; Adriaens and Macken 1995: 123−141). The MAXit Checker is another controlled language software developed by Smart Communications Incorporation to analyse technical texts written in controlled or simplified English with the use of more than 8,500 grammar rules and artificial intelligence to check the clarity, consistency, simplicity, and global acceptance of the texts. The Carnegie Group also produced the ClearCheck, which performs syntactic parsing to detect such grammatical problems as ambiguity (Andersen 1994: 227). Advantages and disadvantages of controlled language The advantages of controlled language translation are numerous, including high readability, better comprehensibility, greater standardization, easier computer processing, greater reusability, increased translatability, improved consistency, improved customer satisfaction, improved competitiveness, greater cost reduction in global product support, and enhanced communication in global management.There are a number of disadvantages in using controlled language, such as expensive system construction, high maintenance cost, time-consuming authoring, and restrictive checking process. Controlled language in use As the advantages of using controlled language outweigh its disadvantages, companies started to use controlled language as early as the 1970s. Examples of business corporations which used controlled languages include Caterpillar Fundamental English (CFE) of the Caterpillar Incorporation in 1975 (Kamprath et al. 1998: 51−61; Lockwood 2000: 187−202), Smart Controlled English of the Smart Communications Ltd in 1975, Douglas Aircraft Company in 1979, the European Association of Aerospace Industries (AECMA) in 1980, the KANT Project at the Center for Machine Translation, Carnegie Mellon University in 1989 (Allen 1995; Carbonell et al. 1992: 225−235; Mitamura et al. 1994: 232−233; Mitamura and Nyberg 1995: 158−172; Mitamura et al. 2002: 244−247; Nyberg and Mitamura 1992: 1069−1073; Nyberg et al. 1997; Nyberg et al. 1998: 1−7; Nyberg and Mitamura 2000: 192−195), the PACE of Perkins Engines Ltd. in 1989, ScaniaSwedish in Sweden in 1995 (Almqvist and Hein 1996: 159−164; Hein 1997), General Motors in 1996, Ericsson English in Sweden in 2000, Nortel Standard English in the United Kingdom in 2002, and Oce Technologies English in Holland in 2002. Controlled language in computer-aided translation systems The concept of controlled language is realized in controlled authoring in computer-aided translation systems. Authoring checking tools are used to check and improve the quality of the source text. There is an automatic rewriting system which is usually used as a tool to realize controlled authoring. One of the computer-aided translation systems that performs controlled S. Chan Computer-aided translation59 authoring is Star Transit. This system provides automatic translation suggestions from the translation memory database from a speedy search engine and it is an open system that can integrate with many authoring systems. Customizability Customizability, etymologically speaking, is the ability to be customized. More specifically, it refers to the ability of a computer or computer-aided translation system to adapt itself to the needs of the user. Customizing a general-purpose machine translation system is an effective way to improve MT quality. Editorial customization Pre-editing is in essence a process of customization. The customization of machine translation systems, which is a much neglected area, is necessary and essential as most software on the market are for general uses and not for specific domains. Practically, system customization can be taken as part of the work of pre-editing as we pre-edit the words and expressions to facilitate the production of quality translation.The degree of customization depends on the goals of translation, and the circumstances and the type of text to be translated. Language customization It is true that there are many language combinations in computer-aided translation systems to allow the user to choose any pair of source and target languages when creating a project, yet many users only work with a limited set of source and target languages. XTM, a cloud-based system, allows the user to set language combinations through the Data section. In the language combinations section, the project administrator or user can reduce and customize the available languages to be used, set the language combinations for the entire system and set specific language combinations for individual customers (XTM International 2012: 15). Language customization in XTM, for example, can be conducted on the Customize tab where there are three options for the user to modify and use language combinations. The first option is ‘system default language combinations’, which is the full set of unmodified language combinations. The second option is ‘system defaults with customized language combinations’, which is the full set of language combinations in which the user may have customized some parameters. The third option is ‘customized language combinations only’, which include only the language combinations that the user has customized. It is possible to add or delete the source and target languages in the selected customized option. Lexicographical customization Lexicographical customization is best shown in the creation of custom dictionaries for each customer, other than the dictionaries for spell checking. This means that multiple translators working on projects for the same customer will use the same custom dictionary. 60 Linguistic customization As far as linguistic customization is concerned, there are basically two levels of customization: lexical customization and syntactical customization. Lexical customization Lexical customization is to customize a machine translation system by preparing a customized dictionary, in addition to the system dictionary, before translating. This removes the uncertainties in translating ambiguous words or word combinations. It must be pointed out, however, that the preparation of a customized dictionary is an enormous task, involving a lot of work in database creation, database maintenance, and database management. Syntactical customization Syntactical customization, on the other hand, is to add sentences or phrases to the database to translate texts with many repetitions. Syntactical customization is particularly important when there is a change of location for translation consumption. The translation memory databases built up in Hong Kong for the translation of local materials, for example, may not be suitable for the production of translations targeted at non-Hong Kong readers, such as those in mainland China. Resource customizationWebsite customization Some computer-aided translation systems allow the user to create resource profile settings. Each profile in Fluency, for example, has four customized uniform resource locators (URLs) associated with it. URLs are the Internet addresses of information. Each document or file on the Internet has a unique address for its location. Fluency allows the user to have four URLs of one’s preference, two perhaps for specialized sites and two general sites. Machine translation system customization Some systems are connected to installed machine translation systems the terminology databases of which can be customized for the generation of output, thus achieving terminological consistency in the target text. Collaborativity Collaborativity is about continuously working and communicating with all parties relating to a translation project, from the client to the reviewer, in a shared work environment to generate the best benefits of team work. Computer-aided translation is a modern mode of translation production that works best in team translation. In the past and decreasingly at present, individual translation has been the norm of practice. At present and increasingly in the future, team translation is the standard practice. A number of systems, such as Across and Wordfast, can allow users to interact with each other through the translation memory server and share translation memory assets in real time. S. Chan Computer-aided translation61 Translation is about management. Translation business operates on projects. Translation technology is about project management, about how work is to be completed by translation teams. With the use of translation technology, the progress of translation work is under control and completed with higher efficiency. The best way to illustrate this point is project collaboration, which allows translators and project managers to easily access and distribute projects and easily monitor their progress. The work of translation in the present digital era is done almost entirely online with the help of a machine translation or computer-aided translation system. This can be illustrated with SDL-Trados 2014, which is a computer-aided translation system developed by SDL International and generally considered to be the most popular translation memory system on the market. Figure 2.17 shows the dashboard of SDL-Trados 2014. Figure 2.17 Dashboard of SDL-Trados 2014 Figure 2.18 List of current projects 62 Figure 2.19 Project details Workflow of a translation project To start a project, the first stage of the workflow is the creation of a termbase and a translation memory database, as shown in Figure 2.20. Figure 2.20 Workflow of a translation project: the first stage S. Chan Computer-aided translation63 In other words, when the Project Manager has any publications, files or web pages to translate, he will send them to the translators of a department or unit, or freelancers for processing. They will create translation units and term databases from these pre-translated documents and save these databases in the SDL-Trados 2014 Server. This is the first stage of the workflow. After the creation of translation memory and term databases, as shown in Figure 2.21, the Project Manager can then initiate a translation project and monitor its progress with the use of SDL-Trados 2014 (as indicated by ). He can assign and distribute source files to in-house and / or freelance translators by emails (as indicated by  ). Translators can then do the translation by (i) reusing the translation memories and terms stored in the databases; (ii) adding new words or expressions to the translation memory and term databases (as indicated by ). When the translation is done, translators send their translated files back to the Project Manager on or before the due date (as indicated by ). When the Project Manager receives the translated files, he updates the project status, finalizes the project and marks it as ‘complete’ (as indicated by ). To make sure that SDL-Trados 2014 has a smooth run, a technical support unit to maintain the SDL-Trados server may be necessary (as indicated by ). Figure 2.21 Workflow of a translation project: the second stage A translation team usually consists of the following members. Project manager A project manager is a professional in the field of project management. The responsibilities of a project manager include the following: 1 plan, execute, and close projects (When planning a project, the project manager works on the overall resources and budget of the project. When executing a project, the project manager can add or import customers and subcontract projects.) 2 create clear and attainable project objectives; 3 build the project requirements; and 4 manage cost, time, and scope of projects. 64 Terminologist A terminologist is one who manages terms in the terminology database. 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