Semi-supervised model adaptation for statistical machine translation

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TypeArticle
Journal titleMachine Translation Journal
Subjectstatistical machine translation; self-training; semi-supervised learning; domain adaptation; model adaptation
AbstractStatistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French-English EuroParl data set and on data from the NIST Chinese-English large-data track. We show a significant improvement in translation quality on both tasks.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number50408
NPARC number5765611
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Record identifier39812cd2-dbe9-4352-9b66-0f484e259af0
Record created2009-03-29
Record modified2016-05-09
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