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

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.

Outline Introduction Symmetric Probabilistic Alignment Experiments and Results Conclusions Future Work

Aligner in the EBMT

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)

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

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.

Outline Introduction Symmetric Probabilistic Alignment Experiments and Results Conclusions Future Work

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

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}

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

Restrictions (1) Untranslated word penalty s7 s8 s9 t2 t3 t4 Anchor Context s6 s7 s8 s9 s10 t1 t2 t3 t4 t5

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.

The SPA CFD

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

Outline Introduction Symmetric Probabilistic Alignment Experiments and Results Conclusions Future Work

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  words long source fragments

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  words long source fragments

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

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

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

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

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

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

Outline Introduction Symmetric Probabilistic Alignment Experiments and Results Conclusions Future Work

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

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

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 The mathematics of statistical machinetranslation: Parameter estimation. Computa-tional Linguistics, 19 (2) : Stephan Vogel, Hermann Ney, and Christoph Till-mann HMM-based word alignment in statistical translation. In COLING '96: The 16th Int. Conf. on Computational Linguistics, pages , Copenhagen, August. I. Dan Melamed. "A Word-to-Word Model of Translational Equivalence". In Procs. of the ACL97. pp Madrid Spain, K. Yamada and K. Knight. A decoder for syntax-based statistical MT. In ACL '02, 2002.

Thank You !! Questions?

Backup Slides Alignment Accuracy Calculation Non-contiguous Alignment

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 =

Non-contiguous Alignment