Improve Statistical Machine Translation with Context-Sensitive Bilingual Semantic Embedding Model

We investigate how to improve bilingual embedding which has been successfully used as a feature in phrase-based statistical machine translation (SMT). Despite bilingual embedding’s success, the contextual information, which is of critical importance to translation quality, was ignored in previous work. To employ the contextual information, we propose a simple and memory-efficient model for learning bilingual embedding, taking both the source phrase and context around the phrase into account. Bilingual translation scores generated from our proposed bilingual embedding model are used as features in our SMT system. Experimental results show that the proposed method achieves significant improvements on large-scale Chinese-English translation task.