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Mixture-modeling with unsupervised clusters for domain adaptation in statistical machine translation


Sennrich, Rico (2012). Mixture-modeling with unsupervised clusters for domain adaptation in statistical machine translation. In: 16th EAMT Conference, Trento, Italy, 28 May 2012 - 29 May 2012, 185-192.

Abstract

In Statistical Machine Translation, in-domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such cases. We apply unsupervised clustering methods to split the original training set, and then use mixture-modeling techniques to build a model adapted to a given target domain. We show that this approach improves performance over an unadapted baseline, and several alternative domain adaptation methods.

Abstract

In Statistical Machine Translation, in-domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such cases. We apply unsupervised clustering methods to split the original training set, and then use mixture-modeling techniques to build a model adapted to a given target domain. We show that this approach improves performance over an unadapted baseline, and several alternative domain adaptation methods.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Language:English
Event End Date:29 May 2012
Deposited On:07 Jun 2012 08:49
Last Modified:23 Nov 2018 08:14
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://hltshare.fbk.eu/EAMT2012/html/Papers/42.pdf
Related URLs:http://www.eamt.org/news/news_program_eamt_2012.php

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