×

A word alignment model based on multiobjective evolutionary algorithms. (English) Zbl 1186.90099

Summary: Word alignment is a key task in statistical machine translation (SMT). This paper presents a novel model for this task. In this model, word alignment is considered as a multiobjective optimization problem and solved based on the non-dominated sorting genetic algorithm II (NSGA-II), which is one of the best multiobjective evolutionary algorithms (MOEA). There are two advantages of the proposed model based on NSGA-II. First, it could be easily extended through incorporating new objective functions. Secondly, it does not need any hand-aligned word-level alignment data to determine the weight of each objective function. Experiments were carried out and the results show that the proposed model outperforms the IBM translation models significantly.

MSC:

90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence

Software:

NSGA-II
Full Text: DOI

References:

[1] P. Koehn, F.J. Och, D. Marcu, Statistical phrase-based translation, in: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL, Edmonton, Canada, 2003, pp. 127-133; P. Koehn, F.J. Och, D. Marcu, Statistical phrase-based translation, in: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL, Edmonton, Canada, 2003, pp. 127-133
[2] Och, F. J.; Ney, H., The alignment template approach to statistical machine translation, Computational Linguistics, 30, 4, 417-499 (2004) · Zbl 1234.68429
[3] D. Chiang, A hierarchical phrase-based model for statistical machine translation, in: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, ACL, Ann Ar-bor, USA, 2005, pp. 263-270; D. Chiang, A hierarchical phrase-based model for statistical machine translation, in: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, ACL, Ann Ar-bor, USA, 2005, pp. 263-270
[4] Y. Liu, Q. Liu, S. Lin, Tree-to-string alignment template for statistical machine translation, in: Proceedings of the 21st International Conference on Computational Linguistics, COLING and 44th Annual Meeting of the Association for Computational Linguistics, ACL, Sydney, Australia, 2006, pp. 609-616; Y. Liu, Q. Liu, S. Lin, Tree-to-string alignment template for statistical machine translation, in: Proceedings of the 21st International Conference on Computational Linguistics, COLING and 44th Annual Meeting of the Association for Computational Linguistics, ACL, Sydney, Australia, 2006, pp. 609-616
[5] Brown, P. F.; Pietra, S. A.D.; Pietra, V. J.D.; Mercer, R. L., The mathematics of statistical machine translation: Parameter estimation, Computational Linguistics, 19, 2, 263-311 (1993)
[6] Och, F. J.; Ney, H., A systematic comparison of various statistical alignment models, Computational Linguistics, 29, 1, 19-51 (2003) · Zbl 1234.68428
[7] J. Tiedemann, Combining clues for word alignment, in: Proceedings of the 10th Conference of European Chapter of the Association for Computational Linguistics, EACL, Budapest, Hungary, 2003, pp. 339-346; J. Tiedemann, Combining clues for word alignment, in: Proceedings of the 10th Conference of European Chapter of the Association for Computational Linguistics, EACL, Budapest, Hungary, 2003, pp. 339-346
[8] C. Cherry, D. Lin, A probability model to improve word alignment, in: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, ACL, Sapporo, Japan, 2003, pp. 88-95; C. Cherry, D. Lin, A probability model to improve word alignment, in: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, ACL, Sapporo, Japan, 2003, pp. 88-95
[9] Y. Liu, Q. Liu, S. Lin, Log-linear models for word alignment, in: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, ACL, Ann Arbor, USA, 2005, pp. 459-466; Y. Liu, Q. Liu, S. Lin, Log-linear models for word alignment, in: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, ACL, Ann Arbor, USA, 2005, pp. 459-466
[10] R.C. Moore, A discriminative framework for bilingual word alignment, in: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language processing, HLT/EMNLP, Vancouver, Canada, 2005, pp. 81-88; R.C. Moore, A discriminative framework for bilingual word alignment, in: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language processing, HLT/EMNLP, Vancouver, Canada, 2005, pp. 81-88
[11] R.C. Moore, Association-based bilingual word alignment, in: Proceedings of the ACL Workshop on Building and Using Parallel Texts, Ann Arbor, USA, 2005, pp. 1-8; R.C. Moore, Association-based bilingual word alignment, in: Proceedings of the ACL Workshop on Building and Using Parallel Texts, Ann Arbor, USA, 2005, pp. 1-8
[12] R.C. Moore, On log-likelihood-ratios and the significance of rare events, in: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP, Barcelona, Spain, 2004, pp. 333-340; R.C. Moore, On log-likelihood-ratios and the significance of rare events, in: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP, Barcelona, Spain, 2004, pp. 333-340
[13] Ker, S. J.; Chang, J. S., A class-based approach to word alignment, Computational Linguistics, 23, 2, 313-343 (1997)
[14] Dice, L. R., Measures of the amount of ecologic association between species, Journal of Ecology, 26, 297-302 (1945)
[15] Zitzler, E.; Thiele, L., Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach, IEEE Transactions on Evolutionary Computation, 3, 4, 257-271 (1999)
[16] Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, 182-197 (2002)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.