Mixing multiple translation models in statistical machine translation

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TypeArticle
Proceedings titleProceedings of the 50th Annual Meeting of the Association for Computational Linguistics
Conference50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012
AbstractStatistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationNational Research Council Canada
Peer reviewedYes
NPARC number20494947
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Record identifier3a79c192-af06-4ff4-84a8-fbc03003c772
Record created2012-08-16
Record modified2016-05-09
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