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A weighted finite state transducer translation template model for statistical machine translation

Authors:

William ByrneAuthors Info & Claims

Natural Language Engineering, Volume 12, Issue 1

Pages 35 - 75

https://doi.org/10.1017/S1351324905003815

Published: 01 March 2006 Publication History

Abstract

We present a Weighted Finite State Transducer Translation Template Model for statistical machine translation. This is a source-channel model of translation inspired by the Alignment Template translation model. The model attempts to overcome the deficiencies of word-to-word translation models by considering phrases rather than words as units of translation. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard finite state machine operations involving these transducers. One of the benefits of using this framework is that it avoids the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We report and analyze bitext word alignment and translation performance on the Hansards French-English task and the FBIS Chinese-English task under the Alignment Error Rate, BLEU, NIST and Word Error-Rate metrics. These experiments identify the contribution of each of the model components to different aspects of alignment and translation performance. We finally discuss translation performance with large bitext training sets on the NIST 2004 Chinese-English and Arabic-English MT tasks.

References

[1]

Allauzen, C., Mohri, M. and Roark, B. (2003) Generalized algorithms for constructing statistical language models. Proceedings 41st Annual Meeting of the Association of Computational Linguistics, pp. 40-47. Sapporo, Japan.

Digital Library

[2]

Bangalore, S. and Riccardi, G. (2001) A finite-state approach to machine translation. Proceedings 2nd meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh, PA.

Digital Library

[3]

Brown, P. F., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Lafferty, J. D., Mercer, R. L. and Roossin, P. S. (1990) A statistical approach to machine translation. Computational Linguistics 16(2): 79-85.

Digital Library

[4]

Brown, P. F., Della Pietra, S. A., Della Pietra, V. J. and Mercer, R. L. (1993) The mathematics of statistical machine translation: parameter estimation. Computational Linguistics 19(2): 263-311.

Digital Library

[5]

Byrne, W., Khudanpur, S., Kim, W., Kumar, S., Pecina, P., Virga, P., Xu, P. and Yarowsky, D. (2003) The Johns Hopkins University 2003 Chinese-English Machine Translation System. Proceedings of MT Summit IX, pp. 447-450. New Orleans, LA.

[6]

Deng, Y. and Byrne, W. (2004) Bitext Chunk Alignment for Statistical Machine Translation. Research Note, Center for Language and Speech Processing, Johns Hopkins University.

[7]

Doddington, G. (2002) Automatic evaluation of machine translation quality using N-gram co-occurrence statistics. Proceedings of the Conference on Human Language Technology, pp. 138-145, San Diego, CA.

Digital Library

[8]

Germann, U., Jahr, M., Knight, K., Marcu, D. and Yamada, K. (2001) Fast decoding and optimal decoding for machine translation. Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, pp. 228-235. Toulouse, France.

Digital Library

[9]

Canadian Parliament (2003) Canadian Hansards. http://www.parl.gc.ca/.

[10]

Knight, K. and Al-Onaizan, Y. (1998) Translation with finite-state devices, Proceedings of the AMTA Conference, pp. 421-437. Langhorne, PA.

[11]

Koehn, P., Och, F. and Marcu, D. (2003) Statistical phrase-based translation. Proceedings of the Conference on Human Language Technology, pp. 127-133. Edmonton, Canada.

Digital Library

[12]

Kumar, S. and Byrne, W. (2002) Minimum Bayes-risk alignment of bilingual texts. Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 140-147. Philadelphia, PA.

Digital Library

[13]

Kumar, S. and Byrne, W. (2003) A weighted finite state transducer implementation of the alignment template model for statistical machine translation. Proceedings of the Conference on Human Language Technology, pp. 142-149. Edmonton, Canada.

Digital Library

[14]

Kumar, S. and Byrne, W. (2004) A weighted finite state transducer translation template model for statistical machine translation. Research Note No. 48, Center for Language and Speech Processing, Johns Hopkins University.

[15]

Kumar, S. and Byrne, W. (2004) Minimum Bayes-risk decoding for statistical machine translation Proceedings of the Conference on Human Language Technology, pp. 169-176. Boston, MA.

[16]

LDC (2002), Chinese Segmenter. http://www.ldc.upenn.edu/Projects/Chinese.

[17]

Marcu, D. and Germann, U. (2002) The ISI ReWrite Decoder Release 0.7.0b. http://www.isi.edu/licensed-sw/rewrite-decoder/.

[18]

Marcu, D. and Wong, W. (2002) A phrase-based, joint probability model for statistical machine translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 133-139. Philadelphia, PA.

Digital Library

[19]

Mohri, M., Pereira, F. and M. Riley (1997), ATT General-purpose finite-state machine software tools. http://www.research.att.com/sw/tools/fsm/

[20]

NIST (2004) The NIST Machine Translation Evaluations. http://www.nist.gov/speech/ tests/mt/.

[21]

Och, F. (2002) Statistical Machine Translation: From Single Word Models to Alignment Templates. PhD Thesis, RWTH Aachen, Germany.

[22]

Och, F. and Ney, H. (2000) Improved statistical alignment models. Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics pp. 440-447. Hong Kong, China.

Digital Library

[23]

Och, F., Tillmann, C. and Ney, H. (1999) Improved alignment models for statistical machine translation. Proceedings of the Joint Conference of Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 20-28. College Park, MD.

[24]

Och, F., Ueffing, N. and Ney, H. (2001) An efficient A^* search algorithm for statistical machine translation. Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, pp. 55-62. Toulouse, France.

Digital Library

[25]

Papineni, K., Roukos, S., Ward, T. and Zhu, W. (2001) Bleu: a Method for Automatic Evaluation of Machine Translation. Technical Report RC22176 (W0109-022), IBM Research Division.

Digital Library

[26]

The People's Daily (2002). http://www.english.people.com.cn.

[27]

Schafer, C. and Yarowsky, D. (2003) Statistical machine translation using coercive two-level syntactic transduction. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Sapporo, Japan.

Digital Library

[28]

Stolcke, A (1998) Entropy-based pruning of backoff language models. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pp. 270-274, Lansdowne, VA.

[29]

Stolcke, A. (2002) SRILM - An Extensible Language Modeling Toolkit. http://www.speech. sri.com/projects/srilm/. Proceedings of the International Conference on Spoken Language Processing, pp. 901-904. Denver, CO.

[30]

Tillmann, C. and Ney, H. (2003) Word reordering and a dynamic programming beam search algorithm for statistical machine translation. Computational Linguistics 29(1): 97-133.

Digital Library

[31]

Tillmann, C. (2003) A projection extension algorithm for statistical machine translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Sapporo, Japan.

Digital Library

[32]

Ueffing, N., Och, F. and Ney, H. (2002) Generation of word graphs in statistical machine translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 156-163. Philadelphia, PA.

Digital Library

[33]

Vogel, S., Ney, H. and Tillmann, C. (1996) HMM based word alignment in statistical translation. Proceedings of the 16th International Conference on Computational Linguistics pp. 836-841. Copenhagen, Denmark.

Digital Library

[34]

Wang, Y. and Waibel, A. (1997) Decoding algorithm in statistical machine translation. Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pp. 366-372. Madrid, Spain.

Digital Library

[35]

JHU (2003), Syntax for Statistical Machine Translation, Final Report, JHU Summer Workshop. http://www.clsp.jhu.edu/ws2003/groups/translate/.

[36]

Zens, R. and Ney, H. (2004) Improvements in phrase-based statistical machine translation. Proceedings of the Conference on Human Language Technology, pp. 257-264. Boston, MA.

[37]

Zhang, Y. Vogel, S. and Waibel A. (2003), Integrated phrase segmentation and alignment model for statistical machine translation. Proceedings of the Conference on Natural Language Processing and Knowledge Engineering. Beijing, China.

Cited By

Marzouk RDe La Higuera CSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)On the tractability of SHAP explanations under Markovian distributionsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693492(34961-34986)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693492
Geneva DShopov GMihov S(2021)Algorithms for Probabilistic and Stochastic Subsequential Failure TransducersImplementation and Application of Automata10.1007/978-3-030-79121-6_11(127-139)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-79121-6_11
Allauzen CByrne Bde Gispert AIglesias GRiley M(2014)Pushdown automata in statistical machine translationComputational Linguistics10.1162/COLI_a_0019740:3(687-723)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1162/COLI_a_00197
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A weighted finite state transducer translation template model for statistical machine translation
1. Computing methodologies
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2. Hardware
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Published In

cover image Natural Language Engineering

Natural Language Engineering Volume 12, Issue 1

March 2006

107 pages

ISSN:1351-3249

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Cambridge University Press

United States

Publication History

Published: 01 March 2006

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Cited By

Marzouk RDe La Higuera CSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)On the tractability of SHAP explanations under Markovian distributionsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693492(34961-34986)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693492
Geneva DShopov GMihov S(2021)Algorithms for Probabilistic and Stochastic Subsequential Failure TransducersImplementation and Application of Automata10.1007/978-3-030-79121-6_11(127-139)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-79121-6_11
Allauzen CByrne Bde Gispert AIglesias GRiley M(2014)Pushdown automata in statistical machine translationComputational Linguistics10.1162/COLI_a_0019740:3(687-723)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1162/COLI_a_00197
Xu PFung PTsujii JHenderson JPasca M(2012)Cross-lingual language modeling with syntactic reordering for low-resource speech recognitionProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2391031(766-776)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2391031
Torres MCasacuberta FConstant MMaletti ASavary A(2011)Stochastic K-TSS bi-languages for machine translationProceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing10.5555/2206310.2206324(98-106)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.5555/2206310.2206324
Iglesias GAllauzen CByrne Wde Gispert ARiley MMerlo PBarzilay RJohnson M(2011)Hierarchical phrase-based translation representationsProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145577(1373-1383)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145577
Lavergne TCrego JAllauzen AYvon FCallison-Burch CKoehn PMonz CZaidan O(2011)From n-gram-based to CRF-based translation modelsProceedings of the Sixth Workshop on Statistical Machine Translation10.5555/2132960.2133035(542-553)Online publication date: 30-Jul-2011
https://dl.acm.org/doi/10.5555/2132960.2133035
González JCasacuberta F(2011)GREATMachine Translation10.1007/s10590-011-9097-625:2(145-160)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1007/s10590-011-9097-6
Dyer CResnik PKaplan R(2010)Context-free reordering, finite-state translationHuman Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics10.5555/1857999.1858127(858-866)Online publication date: 2-Jun-2010
https://dl.acm.org/doi/10.5555/1857999.1858127
de Gispert AIglesias GBlackwood GR. Banga EByrne W(2010)Hierarchical phrase-based translation with weighted finite-state transducers and shallow-n grammarsComputational Linguistics10.1162/coli_a_0000636:3(505-533)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1162/coli_a_00006
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