Partial Matching Strategy for Phrase-based Statistical Machine Translation

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.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Zhongjun He, Qun Liu, Shouxun Lin
• Keywords: ACL 2008, Computational Linguistics, Data Sparseness Problem, Partial Matching Strategy, Phrase-based Statistical Machine