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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, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 30 July 2021

Learning to Combine Machine Translation Systems

Learning to Combine Machine Translation Systems

Chapter:
(p.257) 13 Learning to Combine Machine Translation Systems
Source:
Learning Machine Translation
Author(s):

Evgeny Matusov

Gregor Leusch

Hermann Ney

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262072977.003.0013

This chapter shows how outputs of multiple machine translation (MT) systems may be combined in order to improve translation quality. It presents a method that computes a consensus translation from the aligned MT hypotheses. The method utilizes the enhanced alignment procedure of Matusov et al. (2006), as well as its extensions. The proposed system combination approach was evaluated on well-established Chinese-to-English and Arabic-to-English large-vocabulary translation tasks. Significant improvements in translation quality were obtained when compared with the best individual MT system.

Keywords:   multiple machine translation, translation quality, consensus translation

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