research-article

Lattice BLEU oracles in machine translation

Authors:

Guillaume Wisniewski,

Franccois YvonAuthors Info & Claims

ACM Transactions on Speech and Language Processing (TSLP), Volume 10, Issue 4

Article No.: 18, Pages 1 - 29

https://doi.org/10.1145/2513147

Published: 03 January 2014 Publication History

Abstract

The search space of Phrase-Based Statistical Machine Translation (PBSMT) systems can be represented as a directed acyclic graph (lattice). By exploring this search space, it is possible to analyze and understand the failures of PBSMT systems. Indeed, useful diagnoses can be obtained by computing the so-called oracle hypotheses, which are hypotheses in the search space that have the highest quality score. For standard SMT metrics, this problem is, however, NP-hard and can only be solved approximately. In this work, we present two new methods for efficiently computing oracles on lattices: the first one is based on a linear approximation of the corpus bleu score and is solved using generic shortest distance algorithms; the second one relies on an Integer Linear Programming (ILP) formulation of the oracle decoding that incorporates count clipping constraints. It can either be solved directly using a standard ILP solver or using Lagrangian relaxation techniques. These new decoders are evaluated and compared with several alternatives from the literature for three language pairs, using lattices produced by two PBSMT systems.

References

[1]

Allauzen, A., Bonneau-Maynard, H., Le, H.-S., Max, A., Wisniewski, G., Yvon, F., Adda, G., Crego, J. M., Lardilleux, A., Lavergne, T., and Sokolov, A. 2011. LIMSI&commat;WMT11. In Proceedings of the 6^th Workshop on Statistical Machine Translation. 309--315.

Digital Library

[2]

Allauzen, C., Kumar, S., Macherey, W., Mohri, M., and Riley, M. 2010. Expected sequence similarity maximization. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies. 957--965.

Digital Library

[3]

Allauzen, C., Mohri, M., Riley, M., and Roark, B. 2004. A generalized construction of integrated speech recognition transducers. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'04). Vol. 1, 761--764.

[4]

Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., and Mohri, M. 2007. OpenFst: A general and efficient weighted finite-state transducer library. In Proceedings of the International Conference on Implementation and Application of Automata. Springer, 11--23.

Digital Library

[5]

Arun, A. and Koehn, P. 2007. Online learning methods for discriminative training of phrase based statistical machine translation. In Proceedings of the 11^th Machine Translation Summit. 15--20.

[6]

Auli, M., Lopez, A., Hoang, H., and Koehn, P. 2009. A systematic analysis of translation model search spaces. In Proceedings of the 4^th Workshop on Statistical Machine Translation (StatMT'09). 224--232.

Digital Library

[7]

Blackwood, G., Gispert, A., and Byrne, W. 2010. Efficient path counting transducers for minimum bayes-risk decoding of statistical machine translation lattices. In Proceedings of the ACL Conference Short Papers (ACL-Short'10). 27--32.

Digital Library

[8]

Callison-Burch, C., Koehn, P., Monz, C., and Zaidan, O. 2011. Findings of the 2011 workshop on statistical machine translation. In Proceedings of the 6^th Workshop on Statistical Machine Translation. Association for Computational Linguistics. 22--64.

Digital Library

[9]

Chang, Y.-W. and Collins, M. 2011. Exact decoding of phrase-based translation models through lagrangian relaxation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 26--37.

Digital Library

[10]

Chiang, D. 2012. Hope and fear for discriminative training of statistical translation models. J. Mach. Learn. Res. 98888, 1159--1187.

Digital Library

[11]

Chiang, D., Marton, Y., and Resnik, P. 2008. Online large-margin training of syntactic and structural translation features. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 224--233.

Digital Library

[12]

Crego, J. M., Max, A., and Yvon, F. 2010. Local lexical adaptation in machine translation through triangulation: SMT helping smt. In Proceedings of the 23^rd International Conference on Computational Linguistics (COLING'10). 232--240.

Digital Library

[13]

Crego, J. M. and Yvon, F. 2010. Improving reordering with linguistically informed bilingual n-grams. In Coling: Posters. Coling 2010 Organizing Committee, 197--205.

Digital Library

[14]

Crego, J. M., Yvon, F., and Marino, J. B. 2011. Ncode: An open source bilingual n-gram smt toolkit. Prague Bull. Math. Linguist. 96, 49--58.

[15]

Denkowski, M. and Lavie, A. 2011. Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems. In Proceedings of the 6^th Workshop on Statistical Machine Translation. 85--91.

Digital Library

[16]

Dreyer, M., Hall, K., and Khudanpur, S. 2007. Comparing reordering constraints for smt using efficient bleu oracle computation. In Proceedings of the NAACL-HLT/AMTA Workshop on Syntax and Structure in Statistical Translation (SSST'07). 103--110.

Digital Library

[17]

Gurobi Optimization. 2010. Gurobi optimizer. Version 3.0. http://www.gurobi.com/products/gurobi-optimizer/prior-versions.

[18]

Karp, R. 1972. Reducibility among combinatorial problems. In Complexity of Computer Computations, R. Miller and J. Thatcher, Eds., Plenum Press, New York, 85--103.

[19]

Knight, K. 1999. Decoding complexity in word-replacement translation models. Comput. Linguist. 25, 4, 607--615.

Digital Library

[20]

Koehn, P. 2010. Statistical Machine Translation. Cambridge University Press, Cambridge, UK.

Digital Library

[21]

Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., and Herbst, E. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of the 45^th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions. 177--180.

Digital Library

[22]

Leusch, G., Matusov, E., and Ney, H. 2008. Complexity of finding the bleu-optimal hypothesis in a confusion network. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 839--847.

Digital Library

[23]

Li, Z. and Khudanpur, S. 2009. Efficient extraction of oracle-best translations from hypergraphs. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Companion Volume: Short Papers (NAACLShort'09). 9--12.

Digital Library

[24]

Liang, P., Bouchard-Cote, A., Klein, D., and Taskar, B. 2006. An end-to-end discriminative approach to machine translation. In Proceedings of the 21^st International Conference on Computational Linguistics and the 44^th Annual Meeting of the Association for Computational Linguistics. 761--768.

Digital Library

[25]

Lin, C.-Y. and Och, F. J. 2004. ORANGE: A method for evaluating automatic evaluation metrics for machine translation. In Proceedings of the 20^th International Conference on Computational Linguistics (COLING'04).

Digital Library

[26]

Macherey, W., Och, F., Thayer, I., and Uszkoreit, J. 2008. Lattice-based minimum error rate training for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 725--734.

Digital Library

[27]

Mangu, L., Brill, E., and Stolcke, A. 2000. Finding consensus in speech recognition: Word error minimization and other applications of confusion networks. Comput. Speech Lang. 14, 4, 373--400.

Digital Library

[28]

Mohri, M. 2002. Semiring frameworks and algorithms for shortest-distance problems. J. Autom. Lang. Comb. 7, 3, 321--350.

Digital Library

[29]

Mohri, M. 2009. Weighted automata algorithms. In Handbook of Weighted Automata, M. Droste, W. Kuich, and H. Vogler, Eds., Springer, 213--254.

[30]

Och, F. J. 2003. Minimum error rate training in statistical machine translation. In Proceedings of the 41^st Annual Meeting on Association for Computational Linguistics (ACL'03). 60--167.

Digital Library

[31]

Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of 40^th Annual Meeting of the Association for Computational Linguistics. 311--318.

Digital Library

[32]

Rush, A. M., Sontag, D., Collins, M., and Jaakkola, T. 2010. On dual decomposition and linear programming relaxations for natural language processing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'10). 1--11.

Digital Library

[33]

Snover, M., Dorr, B., Schwartz, R., Micciulla, L., and Makhoul, J. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of the Conference of the Association for Machine Translation in the America (AMTA'06). 223--231.

[34]

Snover, M., Madnani, N., Dorr, B., and Schwartz, R. 2009. Fluency, adequacy, or hter&quest; Exploring different human judgments with a tunable mt metric. In Proceedings of the 4^th Workshop on Statistical Machine Translation. 259--268.

Digital Library

[35]

Sokolov, A., Wisniewski, G., and Yvon, F. 2012. Computing lattice bleu oracle scores for machine translation. In Proceedings of the 13^th Conference of the European Chapter of the Association for Computational Linguistics. 120--129.

Digital Library

[36]

Song, X., Cohn, T., and Specia, L. 2013. BLEU deconstructed: Designing a better mt evaluation metric. In Proceedings of the 14^th International Conference on Intelligent Text Processing and Computational Linguistics.

[37]

Tromble, R. W., Kumar, S., Och, F., and Macherey, W. 2008. Lattice minimum bayes-risk decoding for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'08). 620--629.

Digital Library

[38]

Turchi, M., Bie, T. D., and Cristianini, N. 2008. Learning performance of a machine translation system: A statistical and computational analysis. In Proceedings of the 3^rd Workshop on Statistical Machine Translation. 35--43.

Digital Library

[39]

Turchi, M., Bie, T. D., Goutte, C., and Cristianini, N. 2012. Learning to translate: A statistical and computational analysis. Adv. Artif. Intell. 1.

Digital Library

[40]

Watanabe, T. 2012. Optimized online rank learning for machine translation. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 253--262.

Digital Library

[41]

Wisniewski, G., Allauzen, A., and Yvon, F. 2010. Assessing phrase-based translation models with oracle decoding. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP'10). 933--943.

Digital Library

[42]

Wisniewski, G. and Yvon, F. 2013. Oracle decoding as a new way to analyze phrase-based machine translation. Mach. Trans. 27, 2, 115--138.

Digital Library

[43]

Wolsey, L. 1998. Integer Programming. John Wiley and Sons, New York.

Cited By

Sokolov AHieber FRiezler SGeva STrotman ABruza PClarke CJärvelin K(2014)Learning to translate queries for CLIRProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609539(1179-1182)Online publication date: 3-Jul-2014
https://dl.acm.org/doi/10.1145/2600428.2609539

Index Terms

Lattice BLEU oracles in machine translation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation

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cover image ACM Transactions on Speech and Language Processing

ACM Transactions on Speech and Language Processing Volume 10, Issue 4

December 2013

206 pages

ISSN:1550-4875

EISSN:1550-4883

DOI:10.1145/2560566

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Copyright © 2014 ACM.

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Publication History

Published: 03 January 2014

Accepted: 01 July 2013

Revised: 01 May 2013

Received: 01 January 2013

Published in TSLP Volume 10, Issue 4

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Cited By

Sokolov AHieber FRiezler SGeva STrotman ABruza PClarke CJärvelin K(2014)Learning to translate queries for CLIRProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609539(1179-1182)Online publication date: 3-Jul-2014
https://dl.acm.org/doi/10.1145/2600428.2609539

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