Bilingual Sentiment Consistency for Statistical Machine Translation

In this paper, we explore bilingual sentiment knowledge for statistical machine translation (SMT). We propose to explicitly model the consistency of sentiment between the source and target side with a lexicon-based approach. The experiments show that the proposed model significantly improves Chinese-to-English NIST translation over a competitive baseline.

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