Fast consensus hypothesis regeneration for machine translation

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TypeArticle
Proceedings titleProceedings of the Joint 5th Workshop on Statistical Machine Translation and MetricsMATR
ConferenceJoint 5th Workshop on Statistical Machine Translation and MetricsMATR, July 11-16, 2010, Uppsala, Sweden
Pages1116; # of pages: 6
AbstractThis paper presents a fast consensus hypothesis regeneration approach for machine translation. It combines the advantages of feature-based fast consensus decoding and hypothesis regeneration. Our approach is more efficient than previous work on hypothesis regeneration, and it explores a wider search space than consensus decoding, resulting in improved performance. Experimental results show consistent improvements across language pairs, and an improvement of up to 0.72 BLEU is obtained over a competitive single-pass baseline on the Chinese-to- English NIST task.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number16067298
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Record identifierd7e233a3-9e71-475e-acb1-e6a881b6eaf3
Record created2010-11-03
Record modified2016-05-09
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