Symmetric Probabilistic Alignment

1. Symmetric Probabilistic Alignment Jae Dong Kim Committee: Jaime G. Carbonell Ralf D. Brown Peter J. Jansen

2. Motivation • In the CMU EBMT system, alignment has been less studied compared to the other components. • We want to investigate a new sub-sentential aligner which uses translation probabilities in a symmetric fashion.

3. Outline • Introduction • Symmetric Probabilistic Alignment • Experiments and Results • Conclusions • Future Work

4. Aligner in the EBMT

5. Sub-sentential Alignment • The CMU EBMT system refers to translation examples to translate unknown source sentence • Since it is hard to find an exactly matching example sentence, the system finds the longest match • Encapsulated local context • Local reordering • The aligner should work on fragments (sub-sentences)

6. Need for a new aligner • Relatively less studied compared to the other components • The old aligner • Heuristic based • Builds a correspondence table • Finds the longest target fragment and the shortest target fragment • Checks every substring of the longest one, which includes the shortest one • Fast but doesn’t use probabilities

7. Related Work • IBM models (Brown et al, 93) • HMM (Vogel et al, 96) • Competitive link (Melamed, 97) • Explicit Syntactic Information(Yamada et al, 02) • ISA (Zhang, 03) • The SPA is different from the above in that it aligns sub-sentences using translation probabilities and some heuristics when the boundary of source fragment is given.

8. Outline • Introduction • Symmetric Probabilistic Alignment • Experiments and Results • Conclusions • Future Work

9. Basic Algorithm (1) • Assumptions: • A bilingual probabilistic dictionary is available • Contiguous source fragments are translated into contiguous target fragments • Fragments are translated independently of surrounding context • Given and

10. Basic Algorithm (2) • Assume that we are considering a candidate target fragment 't2 t3 t4' given a source fragment 's7 s8 s9' • Source -> Target Translation Score S_tmp = max( p(t2|s7), p(t3|s7), p(t4|s7), ε ) x max( p(t2|s8), p(t3|s8), p(t4|s8), ε ) x max( p(t2|s9), p(t3|s9), p(t4|s9), ε ) S_st = S_tmp^{1/3}

11. Basic Algorithm (3) • Source <- Target Translation Score S_tmp = max( p(s7|t2), p(s8|t2), p(s9|t2), ε ) x max( p(s7|t3), p(s8|t3), p(s9|t3), ε ) x max( p(s7|t4), p(s8|t4), p(s9|t4), ε ) S_ts = S_tmp^{1/3} • Source <->Target Translation Score Score = S_st * S_ts

12. Restrictions (1) • Untranslated word penalty s7 s8 s9 t2 t3 t4 • Anchor Context s6s7 s8 s9s10s6s7 s8 s9s10 t1t2 t3 t4t5t1t2 t3 t4t5

13. Restrictions (2) • Length penalty • “t2 ... t30” for “s7 s8 s9”. Realistic? • We expect a proportional target fragment length to the source fragment length. • Distance penalty • “t45 t46 t47” for “s7 s8 s9”. Realistic? Maybe. • Between similar word order languages, we might expect a proportional position.

14. The SPA CFD

15. Combined Aligner • Set a threshold for the SPA • The SPA produces results with higher score than the threshold • For each source fragment • If there is a result from the SPA -> use the SPA result • Otherwise, use the IBM result

16. Outline • Introduction • Symmetric Probabilistic Alignment • Experiments and Results • Conclusions • Future Work

17. Alignment Accuracy (1) • Evaluation Metrics • F1 (Precision, Recall) - based on positions • Data • English-Chinese • Xinhua news wire • Training data: 1m sentence pairs • Trained GIZA++ with default parameters • For the SPA, used the dictionary by GIZA++ • Test data: • 366 sentence pairs - 3 copies by 3 people • 20 more sentence pairs - 1 copy by another • 27286 3-8 words long source fragments

18. Alignment Accuracy (2) • Data • French-English • Canadian Hansard • Training data: 1m sentence pairs • Trained GIZA++ with default parameters • For the SPA, used the dictionary by GIZA++ • Test data • 91 sentence pairs • 12466 3-8 words long source fragments

19. Alignment Accuracy (3) • Alignments to be compared • Random: random alignment to a reasonably long target fragment • Positional: alignment to a proportionally positioned target fragment • Oracle: the best possible contiguous human alignment • SPA-uni: unidirectional basic alignment • SPA-basic: bidirectional basic alignment • SPA: the best SPA alignment with restrictions • IBM4: non-contiguous alignment by IBM Model 4 • COMB: the combination of SPA and IBM4 alignments • SPA-top10: the best of top 10 alignment results of SPA

20. Alignment Accuracy : En-Cn • SPA-basic outperformed SPA-uni • SPA was the best when we applied untranslated word penalty and length penalty • Our significance test showed that the difference between IBM4 and COMB is significant

21. Alignment Accuracy : Fr-En • SPA-basic outperformed SPA-uni • SPA was the best when we applied all the restrictions • Our significance test showed that the difference between IBM4 and COMB is not significant

22. Human Alignment Evaluation Rough idea about how much humans agree on alignment

23. EBMT Performance (1) • Data • French-English (Canadian Hansard) • 20k training sentence pairs • Test • Development set: 100 sentence pairs • 2 reference set: 2 references for 100 source sentences • Evaluation set: 10 X 100 sentence pairs • Evaluation Metric • BLEU

24. EBMT performance (2) • SPA, IBM4 and COMB performs significantly better than EBMT (the old aligner) • For 'Test', SPA outperformed EBMT by 28.5 % • Among SPA, IBM4 and COMB, nothing is significantly better than the others

25. Outline • Introduction • Symmetric Probabilistic Alignment • Experiments and Results • Conclusions • Future Work

26. Conclusions • Improvement on EBMT performance • Combined aligner worked the best on English-Chinese set • Bidirectional alignment worked better than unidirectional alignment

27. Future Work • Incorporating human dictionaries to cover more general domains • Non-contiguous alignment • Co-training of the SPA and a dictionary • Experiments on different data sets and different language pairs • Experiments with different metrics • Speed up

28. References • Ying Zhang, Stephan Vogel and Alex Waibel. Integrated Phrase Segmentation and Alignment Model for Statistical Machine Translation. submitted to Proc. of International Confrerence on Natural Language Processing and Knowledge Engineering (NLP-KE), 2003, Beijing, China. • Peter F. Brown, Stephen A. Della Pietra, Vin-cent J. Della Pietra, and Robert L. Mercer. 1993. The mathematics of statistical machinetranslation: Parameter estimation. Computa-tional Linguistics, 19 (2) :263-311. • Stephan Vogel, Hermann Ney, and Christoph Till-mann. 1996. HMM-based word alignment in statistical translation. In COLING '96: The 16th Int. Conf. on Computational Linguistics, pages 836-841, Copenhagen, August. • I. Dan Melamed. "A Word-to-Word Model of Translational Equivalence". In Procs. of the ACL97. pp 490--497. Madrid Spain, 1997. • K. Yamada and K. Knight. A decoder for syntax-based statistical MT. In ACL '02, 2002.

29. Thank You !! Questions?

30. Backup Slides • Alignment Accuracy Calculation • Non-contiguous Alignment

31. Alignment Accuracy Calculation • Human Answer ... under the unemployment insurance plan of the other country ... • Machine Answer ... under the unemployment insurance plan of the other country ... • Precision: 4/5 = 0.2 • Recall: 4/8 = 0.5 • F1 = 0.2857

32. Non-contiguous Alignment