Comparing and Integrating Alignment Template and Standard Phrase-Based Statistical Machine Translation

Authors:
Lin Xu;Xiaoguang Cao;Bufeng Zhang;Mu Li
Affiliations:
Lab of Pattern Recognition and Intelligent System, Image Processing Center, BeiHang University, China;Lab of Pattern Recognition and Intelligent System, Image Processing Center, BeiHang University, China;AI Lab, Computer Science and Technology, Tianjing University, China;Microsoft Research Asia,
Venue:
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Year:
2009

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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.