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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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Linguistically Enriched Word-Sequence Kernels for Discriminative Language Modeling

Linguistically Enriched Word-Sequence Kernels for Discriminative Language Modeling

Chapter:
(p.111) 6 Linguistically Enriched Word-Sequence Kernels for Discriminative Language Modeling
Source:
Learning Machine Translation
Author(s):

Pierre Mahé

Nicola Cancedda

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262072977.003.0006

This chapter introduces a method for taking advantage of background linguistic resources in statistical machine translation. It starts with a brief introduction to word-sequence kernels, followed by a description of the notion of factored representation and details of the kernel formulation. The next section validates the kernel construction on an artificial discrimination task reproducing some of the conditions encountered in translation. The chapter concludes with a discussion of related and future work.

Keywords:   statistical machine translation, word-sequence kernels, factored representation, kernel construction

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