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Bilingual sentiment consistency for statistical machine translation

From National Research Council Canada

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Affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
ConferenceThe 14th Conference of the European Chapter of the Association for Computational Linguistics, April 26-30 2014, Gothenburg, Sweden
SubjectLexicon-based; Statistical machine translation; Computational linguistics
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.
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  • Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, April 26-30 2014 (30 April 2014): 607–615.
LanguageEnglish
Peer reviewedYes
NPARC number21275948
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Record identifierd23168f2-8ee4-4559-8a93-bf5b228c3660
Record created2015-08-12
Record modified2020-06-04
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