Abstract
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. © 2014 Association for Computational Linguistics.
Cite
CITATION STYLE
Chen, B., & Zhu, X. (2014). Bilingual sentiment consistency for statistical machine translation. In 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014 (pp. 607–615). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/e14-1064
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