Modeling Indicative Context for Statistical Machine Translation.

Contextual information is very important to select the appropriate phrases in statistical machine translation (SMT). The selection of different target phrases is sensitive to different parts of source contexts. Previous approaches based on either local contexts or global contexts neglect impacts of different contexts and are not always effective to disambiguate translation candidates. As a matter of fact, the indicative contexts are expected to play more important roles for disambiguation. In this paper, we propose to leverage the indicative contexts for translation disambiguation. Our model assigns phrase pairs confidence scores based on different source contexts which are then intergraded into the SMT log-linear model to help select translation candidates. Experimental results show that our proposed method significantly improves translation performance on the NIST Chinese-to-English translation tasks compared with the state-of-the-art SMT baseline.