1. 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. 2 Overview Motivation Stochastic Finite State Machines Learning Machine Translation Models Case study – MT for Human-Machine Spoken Dialog Experiments and Results

3. 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. 4 Stochastic Machine Translation Noisy-channel paradigm (IBM) Stochastic Finite State Transducer Model MT

5. 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. 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) (  I) (quiero,wanna) (usar,use) (mi,my) (tarjeta,card) (de,  (crédito,credit)

7. 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,I wanna) (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. 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. 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. 10 Lexical Reordering Output of the lexical choice transducer: sequence of target language phrases. –I’d this to my home phone to 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. 11 Lexical Reordering Models Alignment of JEnglish-English sentence pairs. JEnglish: I’d this to my home phone to charge like English: I’d like to charge this to my home phone I’d this like charge home my phone to I’d this like charge home my phone to

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

13. 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. 14 ASR-based Speech Translation Alignment Lexical Choice Phrase Learning Lexical Reordering Acoustic Model Training Lexicon FSM Speech Recognizer

15. 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. 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. 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. 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. 19 Translation Accuracy (English-Japanese on Text) After reordering is better than before reordering Phrase n-grams better than simple n-grams

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

21. 21 Application Driven Evaluation HMIHY Customer Care Service Multilingual client ASR-MT TTS-MT Text(English) MT as front-end to HMIHY –Multilingual enable an existing service English Spanish ……

22. 22 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.

23. 23 MT evaluation on HMIHY Classification accuracy on original English text and Japanese-English translated text.

24. 24 DEMO

25. 25 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

26. 26 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 I’d this like charge home my phone to I’d this like charge home my phone to

27. 27 Lexical Choice Accuracy (Japanese-to-English Text Translation) VNST orderRecall R Precisio n P F-Measure 2*P*R/(P+R) Unigram31.192.2 46.5 Bigram65.489.9 75.8 Trigram63.291.5 74.7 Phrase Unigram41.992.9 57.8 Phrase Bigram66.789.3 76.4 Phrase Trigram65.389.9 75.7

28. 28 Translation Accuracy ( English-Japanese on text ) VNST order Accuracy before Reordering Accuracy after Reordering Unigram23.832.2 Bigram56.969.4 Trigram56.469.1 Phrase Unigram44.046.8 Phrase Bigram60.469.8 Phrase Trigram58.966.7

29. 29 Translation Accuracy English-Japanese on speech (one-best ASR output ) VNST order Accuracy before Reordering Accuracy after Reordering Unigram21.421.7 Bigram48.955.7 Trigram49.056.8 Phrase Unigram39.339.6 Phrase Bigram51.356.5 Phrase Trigram50.956.9

30. 30 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