Abstract
In statistical machine translation (SMT) research, phrase-based methods have been receiving more interest in recent years. In this paper, we first give a brief survey of phrase-based SMT framework, and then make detailed comparisons of two typical implementations: alignment template approach and standard phrase-based approach. At last, we propose an improved model to integrate alignment template into standard phrase-based SMT as a new feature in a log-linear model. Experimental results show that our method outperforms the baseline method.
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References
Tomás, J., Casacuberta, F.: Combining Phrase-based and Template-Based alignment models in Statistical Translation. In: IbPRIA (2003)
Brown, P.F., et al.: The methematics of statistical machine translation:Parameter estimation. Computational Linguistics (1999)
Och, F.J.: Improved Alignment Models for Statistical Machine Translation. In: Proc. of the Joint Conf. of Empirical Methods in Natural Language Processing and Very Large Corpora, June (1999)
Yamada, K., Knight, K.: A syntax-based statistical translation model. In: Proc. Of the 39th Annual Meeting of ACL, Toulouse, France, July (2001)
Marcu, D., Wong, W.: A phrase-based, joint probability model for statistical machine translation. In: Proceeding of EMNLP (2002)
Zens, R., Och, F.J., Ney, H.: Phrase-based Statistical Machine Translation. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) 25th German Conf. on Artificial Intelligence, KI2002 (2002)
Koehn, P., et al.: Statistical Phrase-Based Translation. In: HLT/NAACL (2003)
Och, F.J., Ney, H.: Discriminative Training and Maximum Entropy Models for Statistical Machine Translation. In: ACL (2002)
Och, F.J., Ney, H.: The alignment template approach to statistical machine translation. Accepted for publication in Computational Linguistics (2004)
Koehn, P., et al.: Edinburgh System Description for 2005 IWSLT Speech Translation Evaluation (2005)
Kumar, S., et al.: A weighted finite state transducer translation template model for statistical machine translation. Natural Language Engineering 1(1), 1–41 (2004)
Birch Mayne, A.C.: Scalable Phrase-Based, Joint Probability Model for Statistical Machine Translation. Master of Science Cognitive Science and Natural Language Processing School of Informatics University of Edinburgh (2005)
Chiang, D.: A hierarchical Phrase-Based Model for Statistical Machine Translation. In: Proc. Of the 43rd Annual Meeting of the ACL, June (2005)
Papineni, K.: BLEU: a Method for Automatic Evaluation of Machine Translation. In: Proc. Of the 40th ACL, July (2002)
Och, F.J.: GIZA++: Training of statistical translation models (2000), http://www-i6.informatik.rwth-aachen.de/~och/softeware/GIZA++.html
Koehn, P.: Pharaoh:Training Manual (2004)
Och, F.J.: Minimum Error Rate Training in Statistical Machine Translation. In: ACL (2003)
Koehn, P.: Pharaoh: a Beam Search Decoder for Phrased-based Statsistical Machine Translation Models, User Mannual and Description for version 1.2, Technical report, USC Information Science Institute (August 2004)
Och, F.J.: An Efficient Method for Determining Bilingual Word Classes. In: Proceedings of EACL (1999)
Xiong, D., Liu, Q., Lin, S.: Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation. In: ACL (2006)
Al-Onaizan, Y., Papineni, K.: Distortion Models for Statistical Machine Translation. In: ACL (2006)
Arun, A., et al.: Edinburgh System Description for the 2006 TC-STAR Spoken LanguageTranslation Evaluation (2006)
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Xu, L., Cao, X., Zhang, B., Li, M. (2007). Comparing and Integrating Alignment Template and Standard Phrase-Based Statistical Machine Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_37
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DOI: https://doi.org/10.1007/978-3-540-70939-8_37
Publisher Name: Springer, Berlin, Heidelberg
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