Partial matching strategy for phrase-based statistical machine translation

Authors:
Zhongjun He;Qun Liu;Shouxun Lin
Affiliations:
Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China
Venue:
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Year:
2008

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Abstract

This paper presents a partial matching strategy for phrase-based statistical machine translation (PBSMT). Source phrases which do not appear in the training corpus can be translated by word substitution according to partially matched phrases. The advantage of this method is that it can alleviate the data sparseness problem if the amount of bilingual corpus is limited. We incorporate our approach into the state-of-the-art PBSMT system Moses and achieve statistically significant improvements on both small and large corpora.