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        <title>An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation</title>
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        <namePart type="given">Chenhui</namePart>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>In this paper, we propose a novel domain adaptation method named “mixed fine tuning” for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2061</identifier>
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%0 Conference Proceedings
%T An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
%A Chu, Chenhui
%A Dabre, Raj
%A Kurohashi, Sadao
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F chu-etal-2017-empirical
%X In this paper, we propose a novel domain adaptation method named “mixed fine tuning” for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.
%R 10.18653/v1/P17-2061
%U https://aclanthology.org/P17-2061
%U https://doi.org/10.18653/v1/P17-2061
%P 385-391

Markdown (Informal)

[An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation](https://aclanthology.org/P17-2061) (Chu et al., ACL 2017)

• An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation (Chu et al., ACL 2017)

ACL

• Chenhui Chu, Raj Dabre, and Sadao Kurohashi. 2017. An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 385–391, Vancouver, Canada. Association for Computational Linguistics.