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Machine Translation: Challenges and Approaches

Invited Lecture Introduction to Natural Language Processing Fall 2008. Machine Translation: Challenges and Approaches. Nizar Habash Associate Research Scientist Center for Computational Learning Systems Columbia University.

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Machine Translation: Challenges and Approaches

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  1. Invited LectureIntroduction to Natural Language Processing Fall 2008 Machine Translation:Challenges and Approaches Nizar HabashAssociate Research Scientist Center for Computational Learning Systems Columbia University

  2. Currently, Google offers translations between the following languages:

  3. Thank you for your attention! Questions?

  4. “BBC found similar support”!!!

  5. Road Map • Multilingual Challenges for MT • MT Approaches • MT Evaluation

  6. Multilingual Challenges • Orthographic Variations • Ambiguous spelling • كتب الاولاد اشعاراكَتَبَ الأوْلادُ اشعَاراً • Ambiguous word boundaries • Lexical Ambiguity • Bank  بنك (financial) vs. ضفة(river) • Eat  essen (human) vs. fressen (animal)

  7. conj noun article plural Multilingual Challenges Morphological Variations • Affixation vs. Root+Pattern • Tokenization

  8. Translation Divergences conflation لست am suis هنا I not here Je ne pas ici لست هنا I-am-not here I am not here Je nesuispas ici I notamnot here

  9. Translation Divergences head swap and categorial

  10. Road Map • Multilingual Challenges for MT • MT Approaches • MT Evaluation

  11. Gisting MT ApproachesMT Pyramid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  12. MT ApproachesGisting Example Sobre la base de dichas experiencias se estableció en 1988 una metodología. Envelope her basis out speak experiences them settle at 1988 one methodology. On the basis of these experiences, a methodology was arrived at in 1988.

  13. Gisting Transfer MT ApproachesMT Pyramid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  14. butter Y X MT ApproachesTransfer Example • Transfer Lexicon • Map SL structure to TL structure poner :subj :mod :obj  :subj :obj mantequilla en X :obj Y X puso mantequilla en Y X buttered Y

  15. Gisting Transfer Interlingua MT ApproachesMT Pyramid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  16. MT ApproachesInterlingua Example: Lexical Conceptual Structure (Dorr, 1993)

  17. Gisting Transfer Interlingua MT ApproachesMT Pyramid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  18. Dictionaries/Parallel Corpora Transfer Lexicons Interlingual Lexicons MT ApproachesMT Pyramid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  19. MT ApproachesStatistical vs. Rule-based Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  20. Statistical MT Noisy Channel Model Portions from http://www.clsp.jhu.edu/ws03/preworkshop/lecture_yamada.pdf

  21. Slide based on Kevin Knight’s http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Statistical MT Automatic Word Alignment • GIZA++ • A statistical machine translation toolkit used to train word alignments. • Uses Expectation-Maximization with various constraints to bootstrap alignments Maria no dio una bofetada a la bruja verde Mary did not slap the green witch

  22. Statistical MT IBM Model (Word-based Model) http://www.clsp.jhu.edu/ws03/preworkshop/lecture_yamada.pdf

  23. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Phrase-Based Statistical MT Morgen fliege ich nach Kanada zur Konferenz Tomorrow I will fly to the conference In Canada • Foreign input segmented in to phrases • “phrase” is any sequence of words • Each phrase is probabilistically translated into English • P(to the conference | zur Konferenz) • P(into the meeting | zur Konferenz) • Phrases are probabilistically re-ordered See [Koehn et al, 2003] for an intro. This is state-of-the-art!

  24. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Word Alignment Induced Phrases Maria no dió una bofetada a la bruja verde Mary did not slap the green witch (Maria, Mary) (no, did not) (slap, dió una bofetada) (la, the) (bruja, witch) (verde, green)

  25. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Word Alignment Induced Phrases Maria no dió una bofetada a la bruja verde Mary did not slap the green witch (Maria, Mary) (no, did not) (slap, dió una bofetada) (la, the) (bruja, witch) (verde, green) (a la, the) (dió una bofetada a, slap the)

  26. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Word Alignment Induced Phrases Maria no dió una bofetada a la bruja verde Mary did not slap the green witch (Maria, Mary) (no, did not) (slap, dió una bofetada) (la, the) (bruja, witch) (verde, green) (a la, the) (dió una bofetada a, slap the) (Maria no, Mary did not) (no dió una bofetada, did not slap), (dió una bofetada a la, slap the) (bruja verde, green witch)

  27. Word Alignment Induced Phrases Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Maria no dió una bofetada a la bruja verde Mary did not slap the green witch (Maria, Mary) (no, did not) (slap, dió una bofetada) (la, the) (bruja, witch) (verde, green) (a la, the) (dió una bofetada a, slap the) (Maria no, Mary did not) (no dió una bofetada, did not slap), (dió una bofetada a la, slap the) (bruja verde, green witch)(Maria no dió una bofetada, Mary did not slap) (a la bruja verde, the green witch) …

  28. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Word Alignment Induced Phrases Maria no dió una bofetada a la bruja verde Mary did not slap the green witch (Maria, Mary) (no, did not) (slap, dió una bofetada) (la, the) (bruja, witch) (verde, green) (a la, the) (dió una bofetada a, slap the) (Maria no, Mary did not) (no dió una bofetada, did not slap), (dió una bofetada a la, slap the) (bruja verde, green witch)(Maria no dió una bofetada, Mary did not slap) (a la bruja verde, the green witch) … (Maria no dió una bofetada a la bruja verde, Mary did not slap the green witch)

  29. Slide courtesy of Kevin Knight http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/mt-lecture.ppt Advantages of Phrase-Based SMT • Many-to-many mappings can handle non-compositional phrases • Local context is very useful for disambiguating • “Interest rate”  … • “Interest in”  … • The more data, the longer the learned phrases • Sometimes whole sentences

  30. MT ApproachesStatistical vs. Rule-based vs. Hybrid Source meaning Target meaning Source syntax Target syntax Source word Target word Analysis Generation

  31. MT ApproachesPractical Considerations • Resources Availability • Parsers and Generators • Input/Output compatability • Translation Lexicons • Word-based vs. Transfer/Interlingua • Parallel Corpora • Domain of interest • Bigger is better • Time Availability • Statistical training, resource building

  32. Road Map • Multilingual Challenges for MT • MT Approaches • MT Evaluation

  33. MT Evaluation • More art than science • Wide range of Metrics/Techniques • interface, …, scalability, …, faithfulness, ... space/time complexity, … etc. • Automatic vs. Human-based • Dumb Machines vs. Slow Humans

  34. Human-based Evaluation ExampleAccuracy Criteria

  35. Human-based Evaluation ExampleFluency Criteria

  36. Automatic Evaluation ExampleBleu Metric(Papineni et al 2001) • Bleu • BiLingual Evaluation Understudy • Modified n-gram precision with length penalty • Quick, inexpensive and language independent • Correlates highly with human evaluation • Bias against synonyms and inflectional variations

  37. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideas sleep furiously Gold Standard References all dull jade ideas sleep irately drab emerald concepts sleep furiously colorless immature thoughts nap angrily

  38. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideassleepfuriously Gold Standard References all dull jade ideassleep irately drab emerald concepts sleepfuriously colorless immature thoughts nap angrily Unigram precision = 4/5

  39. Automatic Evaluation ExampleBleu Metric Test Sentence colorless green ideas sleep furiously colorless green ideas sleep furiously colorless greenideas sleepfuriously colorless green ideassleep furiously Gold Standard References all dull jade ideassleep irately drab emerald concepts sleepfuriously colorless immature thoughts nap angrily Unigram precision = 4 / 5 = 0.8 Bigram precision = 2 / 4 = 0.5 Bleu Score = (a1 a2 …an)1/n = (0.8╳ 0.5)½ = 0.6325  63.25

  40. Automatic Evaluation ExampleMETEOR (Lavie and Agrawal 2007) • Metric for Evaluation of Translation with Explicit word Ordering • Extended Matching between translation and reference • Porter stems, wordNet synsets • Unigram Precision, Recall, parameterized F-measure • Reordering Penalty • Parameters can be tuned to optimize correlation with human judgments • Not biased against “non-statistical” MT systems

  41. Metrics MATR Workshop • Workshop in AMTA conference 2008 • Association for Machine Translation in the Americas • Evaluating evaluation metrics • Compared 39 metrics • 7 baselines and 32 new metrics • Various measures of correlation with human judgment • Different conditions: text genre, source language, number of references, etc.

  42. Automatic Evaluation ExampleSEPIA(Habash and ElKholy 2008) • A syntactically-aware evaluation metric • (Liu and Gildea, 2005; Owczarzak et al., 2007; Giménez and Màrquez, 2007) • Uses dependency representation • MICA parser (Nasr & Rambow 2006) • 77% of all structural bigrams are surface n-grams of size 2,3,4 • Includes dependency surface span as a factor in score • long-distance dependencies should receive a greater weight than short distance dependencies • Higher degree of grammaticality?

  43. Interested in MT?? • Contact me (habash@cs.columbia.edu) • Research courses, projects • Languages of interest: • English, Arabic, Hebrew, Chinese, Urdu, Spanish, Russian, …. • Topics • Statistical, Hybrid MT • Phrase-based MT with linguistic extensions • Component improvements or full-system improvements • MT Evaluation • Multilingual computing

  44. Thank You

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