Bilingual Sense Similarity for Statistical Machine Translation

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Proceedings titleProceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Conference48th Annual Meeting of the Association for Computational Linguistics, July 11–16, 2010, Uppsala, Sweden
Pages834843; # of pages: 10
SubjectInformation and Communications Technologies
AbstractThis paper proposes new algorithms to compute the sense similarity between two units (words, phrases, rules, etc.) from parallel corpora. The sense similarity scores are computed by using the vector space model. We then apply the algorithms to statistical machine translation by computing the sense similarity between the source and target side of translation rule pairs. Similarity scores are used as additional features of the translation model to improve translation performance. Significant improvements are obtained over a state-of-the-art hierarchical phrase-based machine translation system.
Publication date
PublisherNational Research Council of Canada
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Access conditionavailable
Peer reviewedYes
NPARC number15736685
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Record identifierc5a3a3fe-7d31-4f97-9d07-8dda09a155b9
Record created2010-07-05
Record modified2016-05-09
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