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A Finite-State Approach to Machine Translation

A Finite-State Approach to Machine Translation. Srinivas Bangalore Giuseppe Riccardi AT&T Labs-Research {srini,dsp3}@research.att.com NAACL 2001, Pittsburgh, June 6, 2001. Overview. Motivation Stochastic Finite State Machines Learning Machine Translation Models Case study

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A Finite-State Approach to Machine Translation

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  1. A Finite-State Approach to Machine Translation Srinivas Bangalore Giuseppe Riccardi AT&T Labs-Research {srini,dsp3}@research.att.com NAACL 2001, Pittsburgh, June 6, 2001

  2. Overview • Motivation • Stochastic Finite State Machines • Learning Machine Translation Models • Case study • MT for Human-Machine Spoken Dialog • Experiments and Results

  3. Motivation • Finite State Transducers (FST) • Unified formalism to represent symbolic transductions • Calculus for combining FSTs • Learnability • Automatically train transductions from (parallel) corpora • Speech-to-Speech Machine Translation chain • Combining speech and language constraints • Previous Approaches to FST-MT: Knight and Al-Onaizan 1998, Vilar et.al. 1999, Ney 2000, Nederhof 2001

  4. Stochastic Machine Translation MT • Noisy-channel paradigm (IBM) • Stochastic Finite State Transducer Model

  5. Pairing and Aligning • Input: Source-Target language sentence pairs • Sentence Alignment (Alshawi, Bangalore and Douglas, 1998) • Output: • Alignment between source-target substrings • Dependency trees for source and target strings Spanish :ajá quiero usar mi tarjeta de crédito English : yeah I wanna use my credit card

  6. Learning SFST from Bi-language • Bi-language: each token consists of a source language word with its target language word. • Ordering of tokens: source language order or target language order • ajá quiero usar mi tarjeta de crédito • yeah I wanna use my credit card • (ajá,yeah) (e,I) (quiero,wanna) (usar,use) (mi,my) (tarjeta,card) (de, e) (crédito,credit)

  7. Learning Bilingual Phrases • Effective translation of text chunks (e.g. collocations) • Learn bilingual phrases • Joint entropy minimization on bi-language corpus • Phrase-segmented bi-language corpus • (ajá,yeah) (quiero,Iwanna) (usar,use) (mi,my) (tarjeta de crédito, card credit) • Local Reordering of phrases tarjeta de crédito card credit credit card Lexical Choice Local Reordering

  8. Local Reordering • Locally reordered phrase=min(S  TLM) • S is the “sausage” FSM • TLM is an n-gram target language model • “credit card” is the more likely phrase

  9. Lexical Choice Model • Train variable N-gram language model (Riccardi 1995) on bi-language corpus. • simple N-gram models • phrase-based N-gram models

  10. Lexical Reordering • Output of the lexical choice transducer: sequence of target language phrases. • I’dthis to my home phoneto charge like • Words in phrases are in target language word order. • However, phrases need to be reordered in target language word order. • Reordered: • I'd like to charge this to my home phone

  11. I’d I’d this this home home like like charge my my phone phone charge to to to to Lexical Reordering Models • Alignment of JEnglish-English sentence pairs. JEnglish: I’d thisto my home phoneto chargelike English: I’d like to charge this to my home phone

  12. Lexical Reordering Models (contd) • Dependency tree represented as a bracketed string with reordering instructions. ….. e:[ e:[to:to e:]e:-1charge:chargee:]e:+1like:like • Train variable N-gram language model on the bracketed corpus • Output of FST: strings with reordering instructions. [ [to ]-1charge]+1 like

  13. Lexical Reordering Models (contd) • Instructions are composed with “interpreter” FST to form target language sentence. • Finite-state approximation: • Well-formedness of brackets checked for a bounded depth with a weighted FSM • Weights are estimated from the bracketed training corpus • Alternate approach: Approximation of a CFG (Nederhof 2001)

  14. ASR-based Speech Translation Acoustic Model Training Alignment Lexical Choice Lexicon FSM Phrase Learning Speech Recognizer Lexical Reordering

  15. MT Evaluation • Application-independent evaluation • Translation Accuracy • Based on string alignment • Application-driven evaluation • “How May I Help You?” • Spoken dialog for call routing (14 call types) • Classification based on salient phrase detection

  16. Examples • Yes I like to make this long distance call area code x x x x x x x x x x • Yeah I need the area code for rockmart georgia • Yeah I’m wondering if you could place this call for me I can’t seem to dial it it don’t seem to want to go through for me

  17. Evaluation Metric • Evaluation metric for MT is a complex issue. • String edit distance between reference string and result string (length in words: R) • Insertions (I) • Deletions (D) • Moves = pairs of Deletions and Insertions (M) • Remaining Insertions (I') and Deletions (D') • Translation Accuracy = 1 – (M + I' + D' + S) / R

  18. Experiments and Evaluation • Data Collection: • The customer side of operator-customer conversations transcribed • Transcriptions were then manually translated into Japanese • Training Set: 12226 English-Japanese sentence pairs • Test Set: 3253 sentences. • Different translation models • Word n-gram and Phrase n-gram

  19. Translation Accuracy(English-Japanese on Text) • After reordering is better than before reordering • Phrase n-grams better than simple n-grams

  20. Translation Accuracy(English-Japanese on Text and Speech) • Speech recognition accuracy 60% • Drop of about 10% between text translation and speech translation

  21. Call-Classification Performance • False Rejection Rate: Probability of rejecting a call, given that the call-type is one of the 14 call-types. • Probability Correct: Probability of correctly classifying a call, given that the call is not rejected.

  22. MT evaluation on HMIHY Classification accuracy on original English text and Japanese-English translated text.

  23. DEMO

  24. Conclusion • Stochastic Finite State based approach is viable and effective for limited domain MT. • Finite-state model chain allows integration of speech and language constraints. • Multilingual speech application enabled by MT http://www.research.att.com/~srini/Projects/Anuvaad/home.html

  25. I’d I’d this this home home like like charge my my phone phone charge to to to to Translation using stochastic FSTs • Sequence of finite-state transductions Japanese: 私はこれを 私の 家の 電話に チャージしたいのです JEnglish: I’d this to my home phone to charge like English: I’d like to charge this to my home phone

  26. Lexical Choice Accuracy(Japanese-to-English Text Translation)

  27. Translation Accuracy(English-Japanese on text)

  28. Translation AccuracyEnglish-Japanese on speech (one-best ASR output)

  29. Biblio -J. Berstel “Transductions and Context Free Languages” Teubner Studienbüchner -G. Riccardi, R. Pieraccini and E. Bocchieri, "Stochastic Automata for Language Modeling", Computer Speech and Language, 10, pp. 265-293, 1996. -Fernando C. N. Pereira and Michael Riley. Speech Recognition by Composition of Weighted Finite Automata . Finite-State Language Processing. MIT Press, Cambridge, Massachusetts. 1997 -S. Bangalore and G. Riccardi, "Stochastic Finite-State Models for Spoken Language Machine Translation", Workshop on Embedded Machine Translation Systems, NAACL, pp. 52-59, Seattle, May 2000. More references on http://www.research.att.com/info/dsp3 http://research.att.com/info/dsp3

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