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Web-Based Machine Translation

Web-Based Machine Translation. Andy Way School of Comput ing Email : away@computing.dcu.ie URL : www.computing.dcu.ie/~away Room: L245 Phone: (700)5644. Plan of Attack (1). What is MT? Why do we do it? How much is it used? How much more could it be used?

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Web-Based Machine Translation

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  1. Web-Based Machine Translation Andy Way School of Computing Email: away@computing.dcu.ie URL: www.computing.dcu.ie/~away Room: L245 Phone: (700)5644

  2. Plan of Attack (1) • What is MT? • Why do we do it? How much is it used? How much more could it be used? • Is it any good? What exactly is it good for? What is it not good for? • What MT methods are there? • Do on-line MT systems translate word-for-word? How might we be able to tell?

  3. Plan of Attack (2) • Do pairs of on-line MT systems work the same in both directions? • How can we help these MT systems help us? • The Future (?!) • Further Reading/More Information

  4. What is MT? MT = FAHQMT MAHT (on-line dictionaries, termbanks, TM etc …) CAT HAMT (resolving ambiguity etc …)

  5. Why do we do MT? • To communicate in other languages than the ones we know … • (If we’re a company) To increase/maintain market share • To speed up the translation process • etc etc ...

  6. How much is it used? • In 2000, MT specialist Scott Bennett said “Altavista's BabelFish... initiated in late 1997, is now used a million times per day”. • In 2001, Softissimo announced that the Internet translation request volume processed by its Reverso translation engine (www.reverso.net) has now reached several million translation requests (of Web pages, e-mail, short texts and results of search engine requests) per month on its mail translation portal and the portals of its Internet partners.“ • V.d. Meer (2003) "Every day, portals like Altavista and Google process nearly 10 million requests for automatic translation."

  7. How much more could it be used? • Volume of text required to be translated currentlyexceeds translators’ capacity (demand outstrips supply). Thisimbalance will only get worse, cf. accession of new Member states inEU. • NB, also Official Languages Act 2003 Solution: automation (the onlysolution).

  8. How much more could it be used? • translation and localisation industry have focussed onproduct documentation whichrepresents probably less than 20%of all text-based information repositories that need to belocalised • time: five times the volume of text needs to be translatedin practically no time. Corporate decision makers will have to begin supporting multilingualcommunication initiatives and strategies.

  9. How much more could it be used? • GIL market growing from $4.2 billion in 2001 to $8.9billion in 2006, an annual growth rate of 16.3%. Localisation and translationservices form by far the largest part of this market with 69.8% of thetotal, i.e. $2.9 billion in 2001 and $5.8 billion in 2006, an annualgrowth rate of 14.6%. • W.r.t. crosslingual applications, expected to grow from lessthan 1% of the total market in 2001 ($42 million) to $193 million in2006, 35% annual growth.

  10. Is MT any good? (1) Depends … what you want to use it for and how you use it!! Cost Input MT Output

  11. Is MT any good? (2) • No pre-editing  Lots of post-editing! • Lots of pre-editing  No(t much) post-editing! GARBAGE IN, GARBAGE OUT!!!

  12. Is MT any good? (3) • Sometimes no pre-editing is required: • for gisting; • for company-internal circulation; • etc etc … • What it’s not good for is literary translation, i.e. won’t take translators’ jobs - will free them up for new (more interesting) tasks and create new niche markets

  13. MT Developers • So MT is of use, and will become used much more than it is currently, so … • … we need people out there who can improve current systems and develop new ones.  let’s look at how people currently “design” MT systems …

  14. MT Methods MT Rule-Based MT Data-Driven MT Transfer Interlingua EBMT SMT

  15. The Vauquois Pyramid for MT Interlingua Analysis Transfer Generation $_source Direct $_target

  16. Examples of MT methods: Transfer English SVO, Irish VSO, Japanese SOV. So translation between them is complicated by facts about word order. But at a ‘deeper’ level, the languages are more similar ...

  17. Transfer (cont’d) e.g. John saw MaryChonaic Seán Máire S S HEAD SUBJOBJGOV SUBJ OBJ see John Mary feic Seán Máire

  18. Examples of MT methods: Transfer e.g. John likes Mary  Marie plaît à Jean (SUBJ) (OBJ) (SUBJ) (IOBJ) Rule: like(A1,A2)  plaire(A2’,A1’). i.e. arguments are switched.

  19. Examples of MT methods: Interlingua John likes Mary  Marie plaît à Jean lex=like/plaire sem=Experiencer sem=Patient lex=John/Jean lex=Mary/Marie

  20. Examples of MT methods: EBMT Data-driven, compiles probabilities for translations … Needs: • bilingual aligned corpora; • find best match(es) of $_source; • establish translational equivalents; • recombine to generate $_target.

  21. EBMT - translation chunks • Sentence aligned: The man swims  L’homme nage. The woman laughs  La femme rit. • Sub-sententially aligned: the man  L’homme, swims  nage, the  l’, man  homme, the  la, woman  femme, laughs  rit ...

  22. EBMT: deriving translations Let’s now translate The man laughs … Best matches: • the man  L’homme • laughs  rit Combined together, we get: L’homme rit Great, can you see any problems?! We can fix these by looking on the Web …

  23. Web Validation of Translations Inputstring: the personal computers Chunks retrieved: • personal computers  ordinateurs personnels • the  le /la/ l’/ les Via Altavista, we get: • Les ordinateurs personnels: 980 hits • L’ ordinateurs personnels: 0 hits • La ordinateurs personnels: 0 hits • Le ordinateurs personnels: 0 hits

  24. Examples of MT methods: SMT Needs: • bilingual aligned corpora; • statistical models of languages and translation. Works by assuming that French is like English in a noisy channel, i.e. in code! cf. Speech Processing models!

  25. Examples of MT methods: Hybridity Rule-based Methods: • generate good translations (if it works!); • encode rule-based phenomena: sent(Num) nounphrase(Num), verbphrase(Num).

  26. Examples of MT methods: Hybridity Statistical Methods: • are robust; • can get a lot right automatically; • don’t need specialised linguistic knowledge of source, target, and how they relate to one another. So let’s choose the best bits from each ...

  27. Do MT systems translate word-for-word? translate([Head1| Tail1], [Head2|Tail2):- biling_lex (Head1,Head2), translate (Tail1, Tail2). biling_lex(john,jean). biling_lex(swims,nage). etc etc …. Well, the MT systems we’re using are a black box (as opposed to a glass box), so we can’t look at the rules to tell definitively …

  28. Translating word-for-word How can we tell then? Compare the input and the output for a suite of test sentences and try and work out what’s going on …

  29. Translating word-for-word If on-line MT systems did translate word-for-word, they would: • pick the most likely translation of each word each time (i.e. no translational variation ever); • we could build up the translation of the sentence compositionally. • Let’s see if this is what happens by looking at some real systems ...

  30. Translating word-for-word Let’s translate We have just finished reading this book French Word-for word we get (from Babelfish): we:nous, have:ayez, just:juste, finished:fini, reading:lecture,this:ceci,book:livre Model 0 Translation: Nous ayez juste fini lecture ceci livre - hopeless!

  31. Translating word-for-word Let’s give the MT system larger chunks: we have:nous avons, just finished reading: lecture finie just, this book:ce livre have just finished reading: ont juste fini la lecture have just … this book: ont juste … ce livre

  32. Translating word-for-word Typing in the whole sentence, we get: nous avons juste fini de lire ce livre, not bad! Capitalizing the ‘we’ and adding a fullstop makes no difference to the translation here. Oracle translation: nous venons de finir de lire ce livre, so you can see Babelfish hasn’t done too badly here ...

  33. Translating word-for-word Let’s try another sentence, The thief was kicking the policeman Word-for-word we get (from Reverso): the:le, thief:Voleur, was:Était, kicking:coup de pied, policeman:policier Model 0 Translation: le Voleur Était coup de pied le Policier, not very good!

  34. Translating word-for-word Building the translation up compositionally: the thief:Le voleur, was kicking:Donnait un coup de pied, the policeman:Le policier Final translation: Le voleur donnait un coup de pied le policier, pretty good!

  35. ENFR = FR EN?! • That is, do both components use the same rules and dictionaries? • Are the translation components reversible? • Are the structural and lexical rules bidirectional? Only one way to find out … let’s see!

  36. ENFR = FR EN?! For our 2 strings, we get: Babelfish: Nous venons de finir de lire ce livre Reverso: Nous venons de finir de lire ce livre --------------------------------------------------------------- Reverso: Le voleur donnait un coup de pied au policier Babelfish: Le voleur donnait un coup de pied le policier

  37. ENFR = FR EN?! Let’s see the pairwise translations. Babelfish: We have just finished reading this book Nous avons juste fini de lire ce livre Nous venons de finir de lire ce livre  We have just finished reading this book Aha!

  38. ENFR = FR EN?! Babelfish, 2nd sentence pair: The thief was kicking the policeman Le voleur donnait un coup de pied le policier Le voleur donnait un coup de pied au policier  The robber gave a kick to the police officer Aha!

  39. ENFR = FR EN?! Reverso, 1st sentence pair: We have just finished reading this book Nous venons de finir de lire ce livre Nous venons de finir de lire ce livre  We have just stopped reading this book Aha!

  40. ENFR = FR EN?! Reverso, 2nd sentence pair: The thief was kicking the policeman Le voleur donnait un coup de pied au policier Le voleur donnait un coup de pied au policier The thief kicked the policeman Aha!

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