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Backward and trigger-based language models for statistical machine translation

Published online by Cambridge University Press:  24 July 2013

DEYI XIONG  and
MIN ZHANG
DEYI XIONG
Affiliation:
School of Computer Science and Technology, Soochow University, Suzhou 215006, China email: dyxiong@suda.edu.cn, minzhang@suda.edu.cn
MIN ZHANG
Affiliation:
School of Computer Science and Technology, Soochow University, Suzhou 215006, China email: dyxiong@suda.edu.cn, minzhang@suda.edu.cn
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Abstract

The language model is one of the most important knowledge sources for statistical machine translation. In this article, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We introduce algorithms to integrate the two proposed models into two kinds of state-of-the-art phrase-based decoders. Our experimental results on Chinese/Spanish/Vietnamese-to-English show that both models are able to significantly improve translation quality in terms of BLEU and METEOR over a competitive baseline.

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Articles
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Natural Language Engineering , Volume 21 , Issue 2 , March 2015 , pp. 201 - 226
Copyright
Copyright © Cambridge University Press 2013 

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