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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: 29 June 2022

Kernel-Based Machine Translation

Kernel-Based Machine Translation

(p.169) 9 Kernel-Based Machine Translation
Learning Machine Translation

Zhuoran Wang

John Shawe-Taylor

The MIT Press

This chapter presents a novel framework for machine translation based on kernel ridge regression. As a kernel method, the framework has the advantage of capturing the correspondences among the features of inputs and outputs in a very high-dimensional space. But the drawback is that its computational complexities are much higher than probabilistic models. A solution is sparse approximation, which poses the problem of extracting a sufficient amount of relevant bilingual training samples for a given input. Other essential improvements to this model could be the integration of additional language models and the utilization of linguistic knowledge.

Keywords:   machine translation, kernel ridge regression, sparse approximation, decoding, language processing

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