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Learning Machine Translation$
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Cyril Goutte, Nicola Cancedda, Marc Dymetman, and George Foster

Print publication date: 2008

Print ISBN-13: 9780262072977

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262072977.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 28 May 2022

Semisupervised Learning for Machine Translation

Semisupervised Learning for Machine Translation

(p.237) 12 Semisupervised Learning for Machine Translation
Learning Machine Translation

Nicola Ueffing

Gholamreza Haffari

Anoop Sarkar

The MIT Press

This chapter proposes algorithms for semisupervised learning. It translates sentences from the source language and then uses them to retrain the statistical machine translation (SMT) system in the hopes of getting a better translation system. It presents detailed experimental evaluations using French-English and Chinese-English data. The French-English translation task used bilingual data from the Europarl corpus, and monolingual data from the same domain as the test set which is drawn from the Canadian Hansard corpus. The Chinese-English task used bilingual data from the NIST large-data track and monolingual data from the Chinese Gigaword corpus.

Keywords:   statistical machine translation, semisupervised learning, Chinese, French, English, language processing

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