Transductive learning for statistical machine translation

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TypeArticle
ConferenceProceedings of Association for Computational Linguistics (ACL) Second Workshop on Statistical Machine Translation (WMT07), June 23, 2007., Prague, Czech Republic
AbstractStatistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text in the target language. In this paper we explore the use of transductive semi-supervised 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 number50336
NPARC number8913432
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Record identifier57de7726-26b5-4857-a38b-e8f882ad3dd7
Record created2009-04-22
Record modified2016-05-09
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