Bilingual sentiment consistency for statistical machine translation

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Proceedings titleEACL 2014
ConferenceProceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, April 26-30 2014, Gothenburg, Sweden
Pages607615; # of pages: 9
SubjectLexicon-based; Statistical machine translation; Computational linguistics
AbstractIn 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.
Publication date
PublisherAssociation for Computer Linguistics
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21275948
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Record identifierd23168f2-8ee4-4559-8a93-bf5b228c3660
Record created2015-08-12
Record modified2016-05-09
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