Paper: Bilingual Sentiment Consistency for Statistical Machine Translation

ACL ID E14-1064
Title Bilingual Sentiment Consistency for Statistical Machine Translation
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 mod- el significantly improves Chinese-to-English NIST translation over a competitive baseline.