Abstract
Word deletion (WD) errors can lead to poor comprehension of the meaning of source translated sentences in phrase-based statistical machine translation (SMT), and have a critical impact on the adequacy of the translation results generated by SMT systems. In this paper, first we classify the word deletion into two categories, wanted and unwanted word deletions. For these two kinds of word deletions, we propose a maximum entropy based word deletion model to improve the translation quality in phrase-based SMT. Our proposed model are based on features automatically learned from a real-word bitext. In our experiments on Chinese-to-English news and web translation tasks, the results show that our approach is capable of generating more adequate translations compared with the baseline system, and our proposed word deletion model yields a +0.99 BLEU improvement and a \(-2.20\) TER reduction on the NIST machine translation evaluation corpora.
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Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, 8–13 December 2014, Montreal, Quebec, Canada, pp. 3104–3112 (2014)
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, 17–21 September 2015, Lisbon, Portugal, pp. 1412–1421(2015)
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL, 7–12 August 2016, Berlin, Germany, vol. 1: Long Papers (2016)
Britz, D., Goldie, A., Luong, M., Le, Q.V.: Massive exploration of neural machine translation architectures. CoRR abs/1703.03906 (2017)
Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2003, Edmonton, Canada, 27 May-1 June 2003 (2003)
Och, F.J., Ney, H.: The alignment template approach to statistical machine translation. Comput. Linguist. 30(4), 417–449 (2004)
Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 6–12 July 2002, Philadelphia, PA, USA, pp. 311–318 (2002)
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of Association for Machine Translation in the Americas, vol. 200 (2006)
Vilar, D., Xu, J., d’Haro, L.F., Ney, H.: Error analysis of statistical machine translation output. In: Proceedings of LREC, pp. 697–702 (2006)
Chiang, D.: Hierarchical phrase-based translation. Comput. Linguist. 33(2), 201–228 (2007)
Galley, M., Hopkins, M., Knight, K., Marcu, D.: What’s in a translation rule? In: Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2004, Boston, Massachusetts, USA, 2–7 May 2004, pp. 273–280 (2004)
Koehn, P., Schroeder, J.: Experiments in domain adaptation for statistical machine translation. In: Proceedings of the Second Workshop on Statistical Machine Translation. Association for Computational Linguistics, pp. 224–227 (2007)
Och, F.J., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 6–12 July 2002, Philadelphia, PA, USA, pp. 295–302 (2002)
Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, 7–12 July, Sapporo Convention Center, Sapporo, pp. 160–167 (2003)
Xiong, D., Liu, Q., Lin, S.: Maximum entropy based phrase reordering model for statistical machine translation. In: ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17–21 July 2006 (2006)
Malouf, R.: A comparison of algorithms for maximum entropy parameter estimation. In: Proceedings of the 6th Conference on Natural Language Learning, CoNLL 2002, Held in cooperation with COLING 2002, Taipei, Taiwan (2002)
Och, F.J., Ney, H.: Improved statistical alignment models. In: 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, China, 1–8 October 2000 (2000)
Xiao, T., Zhu, J., Zhang, H., Li, Q.: Niutrans: an open source toolkit for phrase-based and syntax-based machine translation. In: The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations, 10 July 2012, Jeju Island, Korea, pp. 19–24 (2012)
Huang, L., Chiang, D.: Forest rescoring: faster decoding with integrated language models. In: Annual Meeting-Association For Computational Linguistics, vol. 45, p. 144 (2007)
Li, C.H., Zhang, D., Li, M., Zhou, M., Zhang, H.: An empirical study in source word deletion for phrase-based statistical machine translation. In: Proceedings of the Third Workshop on Statistical Machine Translation. Association for Computational Linguistics, pp. 1–8 (2008)
Li, Q., Zhang, D., Li, M., Xiao, T., Zhu, J.: Better addressing word deletion for statistical machine translation. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS, vol. 10102, pp. 91–102. Springer, Cham (2016). doi:10.1007/978-3-319-50496-4_8
Parton, K., Habash, N., McKeown, K., Iglesias, G., De Gispert, A.: Can automatic post-editing make mt more meaningful? In: Proceedings of the 16th Annual Conference of the European Association for Machine Translation, EAMT 2012, pp. 111–118 (2012)
Huck, M., Ney, H.: Insertion and deletion models for statistical machine translation. In: Proceedings of Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 3–8 June 2012, Montréal, Canada, pp. 347–351 (2012)
Zhang, Y., Matusov, E., Ney, H.: Are unaligned words important for machine translation? In: Proceedings of The 13th Annual Conference of the EAMT, vol. 226, p. 233 (2009)
Liu, Y., Liu, Q., Lin, S.: Discriminative word alignment by linear modeling. Computat. Linguist. 36(3), 303–339 (2010)
Zhu, J., Li, Q., Xiao, T.: Improving syntactic rule extraction through deleting spurious links with translation span alignment. Nat. Lang. Eng. 21(2), 227–249 (2015)
Menezes, A., Quirk, C.: Syntactic models for structural word insertion and deletion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 735–744 (2008)
Acknowledgements
This work was supported in part by the National Science Foundation of China (61672138 and 61432013), the Fundamental Research Funds for the Central Universities, and the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research.
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Li, Q., Han, Y., Xiao, T., Zhu, J. (2017). Context Sensitive Word Deletion Model for Statistical Machine Translation. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_7
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DOI: https://doi.org/10.1007/978-3-319-69005-6_7
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