Abstract
Word deletion (WD) problems have a critical impact on the adequacy of translation and can lead to poor comprehension of lexical meaning in the translation result. This paper studies how the word deletion problem can be handled in statistical machine translation (SMT) in detail. We classify this problem into desired and undesired word deletion based on spurious and meaningful words. Consequently, we propose four effective models to handle undesired word deletion. To evaluate word deletion problems, we develop an automatic evaluation metric that highly correlates with human judgement. Translation systems are simultaneously tuned for the proposed evaluation metric and BLEU using minimum error rate training (MERT). The experimental results demonstrate that our methods achieve significant improvements in word deletion problems on Chinese-to-English translation tasks.
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References
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of ACL, pp. 311–318 (2002)
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: Proceedings of HLT-NAACL, 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, pp. 224–227 (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, pp. 1–8 (2008)
Och, F.J., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of ACL, pp. 295–302 (2002)
Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp. 65–72 (2005)
Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of ACL, pp. 160–167 (2003)
Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of HLT-NAACL, pp. 48–54 (2003)
Zhang, Y., Matusov, E., Ney, H.: Are unaligned words important for machine translation? In: Proceedings of EAMT, pp. 226–233 (2009)
Xiao, T., Zhu, J., Zhang, H., Li, Q.: NiuTrans: an open source toolkit for phrase-based and syntax-based machine translation. In: Proceedings of ACL 2012 System Demostrations, pp. 19–24 (2012)
Xiong, D., Liu, Q., Lin, S.: Maximum entropy based phrase reordering model for statistical machine translation. In: Proceedings of ACL, pp. 521–528 (2006)
Huang, L., Chiang, D.: Forest rescoring: faster decoding with integrated language models. In: Proceedings of ACL, pp. 144–151 (2007)
Och, F.J., Ney, H.: Improved statistical alignment models. In: Proceedings of ACL, pp. 440–447 (2000)
Menezes, A., Quirk, C.: Syntactic models for structural word insertion and deletion. In: Proceedings of EMNLP, pp. 735–744 (2008)
Hermjakob, U.: Improved word alignment with statistics and linguistic heuristics. In: Proceedings of EMNLP, pp. 229–237 (2009)
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)
Liu, Y., Liu, Q., Lin, S.: Discriminative word alignment by linear modeling. Comput. Linguist. 36(3), 303–339 (2010)
Deng, Y., Zhou, B.: Optimizing word alignment combination for phrase table training. In: Proceedings of ACL-IJCNLP, pp. 229–232 (2009)
Parton, K., Habash, N., McKeown, K., Iglesias, G., de Gispert, A.: Can automatic post-editing make MT more meaningful? In: Proceedings of EAMT, pp. 111–118 (2012)
Huck, M., Ney, H.: Insertion and deletion models for statistical machine translation. In: Proceedings of HLT-NAACL, pp. 347–351 (2012)
Acknowledgements
This work was done while the first author was visiting the machine translation group at Microsoft Research Asia, and was mainly supported by the Fundamental Research Funds for the Central Universities under Grant No. N140406003, the China Scholarship Council, and the National Natural Science Foundation of China under Grant No. 61272376, No. 61300097 and No. 61432013.
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Li, Q., Zhang, D., Li, M., Xiao, T., Zhu, J. (2016). Better Addressing Word Deletion for Statistical Machine Translation. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_8
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DOI: https://doi.org/10.1007/978-3-319-50496-4_8
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