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Domain adaptation strategies in statistical machine translation: a brief overview

Published online by Cambridge University Press:  30 October 2015

Marta R. Costa-Jussà
Marta R. Costa-Jussà*
Affiliation:
Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, State of Mexico, Mexico e-mail: marta@nlp.cic.ipn.edu
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Abstract

Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.

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Articles
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The Knowledge Engineering Review , Volume 30 , Issue 5 , November 2015 , pp. 514 - 520
Copyright
© Cambridge University Press, 2015 

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