Abstract
Reordering is of essential importance for phrase based statistical machine translation. In this paper, we would like to present a new method of reordering in phrase based statistical machine translation. We inspired from [1] using preprocessing reordering approaches. We used shallow parsing and transformation rules to reorder the source sentence. The experiment results from English-Vietnamese pair showed that our approach achieves significant improvements over MOSES which is the state-of-the art phrase based system.
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References
Xia, F., McCord, M.: Improving a Statistical MT System with Automatically Learned Rewrite Patterns. In: Proceedings of Coling 2004, Geneva, Switzerland, August 23–August 27, pp. 508–514 (2004)
Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of HLT-NAACL 2003, Edmonton, Canada, pp. 127–133 (2003)
Och, F.J., Ney, H.: The alignment template approach to statistical machine translation. Computational Linguistics 30(4), 417–449 (2004)
Chiang, D.: A hierarchical phrase-based model for statistical machine translation. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), Ann Arbor, Michigan, pp. 263–270 (June 2005)
Collins, M., Koehn, P., Kucerová, I.: Clause restructuring for statistical machine translation. In: Proc. ACL 2005, Ann Arbor, USA, pp. 531–540 (2005)
Quirk, C., Menezes, A., Cherry, C.: Dependency treelet translation: Syntactically informed phrasal smt. In: Proceedings of ACL 2005, Ann Arbor, Michigan, USA, pp. 271–279 (2005)
Huang, L., Mi, H.: Efficient incremental decoding for tree-to-string translation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 273–283. Association for Computational Linguistics, Cambridge (2010)
Xu, P., Kang, J., Ringgaard, M., Och, F.: Using a dependency parser to improve smt for subject-object-verb languages. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 245–253. Association for Computational Linguistics, Boulder (2009)
Talbot, D., Kazawa, H., Ichikawa, H., Katz-Brown, J., Seno, M., Och, F.: A lightweight evaluation framework for machine translation reordering. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 12–21. Association for Computational Linguistics, Edinburgh (2011)
Katz-Brown, J., Petrov, S., McDonald, R., Och, F., Talbot, D., Ichikawa, H., Seno, M., Kazawa, H.: Training a parser for machine translation reordering. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, pp. 183–192. Association for Computational Linguistics, Scotland (2011)
Nguyen, T.P., Shimazu, A.: Improving phrase-based smt with morpho-syntactic analysis and transformation. In: Proceedings AMTA 2006 (2006)
Wang, C., Collins, M., Koehn, P.: Chinese syntactic reordering for statistical machine translation. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 737–745. Association for Computational Linguistics, Prague (2007)
Habash, N.: Syntactic preprocessing for statistical machine translation. Proceedings of the 11th MT Summit (2007)
Zhang, Y., Zens, R., Ney, H.: Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation. In: Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation, pp. 1–8 (2007)
Nguyen, P.T., Shimazu, A., Nguyen, L.-M., Nguyen, V.-V.: A syntactic transformation model for statistical machine translation. International Journal of Computer Processing of Oriental Languages (IJCPOL) 20(2), 1–20 (2007)
Tsuruoka, Y., Tsujii, J., Ananiadou, S.: Fast full parsing by linear-chain conditional random fields. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009, pp. 790–798. Association for Computational Linguistics, Stroudsburg (2009)
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., Herbst, E.: Moses: Open source toolkit for statistical machine translation. In: Proceedings of ACL, Demonstration Session (2007)
Nguyen, T.P., Shimazu, A., Ho, T.B., Nguyen, M.L., Nguyen, V.V.: A tree-to-string phrase-based model for statistical machine translation. In: Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL 2008), Coling 2008 Organizing Committee, pp. 143–150 (August 2008)
Stolcke, A.: Srilm - an extensible language modeling toolkit. In: Proceedings of International Conference on Spoken Language Processing, vol. 29, pp. 901–904 (2002)
Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, pp. 311–318 (July 2002)
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Vuong, HT., Tu, D.N., Le Nguyen, M., Van Nguyen, V. (2012). Shallow Syntactic Preprocessing for Statistical Machine Translation. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_11
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DOI: https://doi.org/10.1007/978-3-642-33983-7_11
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