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Perplexity minimization for translation model domain adaptation in statistical machine translation


Sennrich, Rico (2012). Perplexity minimization for translation model domain adaptation in statistical machine translation. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon, France, April 2012 - 2012, 539-549.

Abstract

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4–10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.

Abstract

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4–10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.

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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:2012
Deposited On:16 Apr 2012 10:32
Last Modified:08 Aug 2017 09:23
Publisher:Association For Computational Linguistics
Funders:National Science Foundation
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.aclweb.org/anthology/E12-1055

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