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        <title>A Survey of Domain Adaptation for Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Chenhui</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.</abstract>
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        <url>https://aclanthology.org/C18-1111/</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T A Survey of Domain Adaptation for Neural Machine Translation
%A Chu, Chenhui
%A Wang, Rui
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F chu-wang-2018-survey
%X Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.
%U https://aclanthology.org/C18-1111/
%P 1304-1319

Markdown (Informal)

[A Survey of Domain Adaptation for Neural Machine Translation](https://aclanthology.org/C18-1111/) (Chu & Wang, COLING 2018)

• A Survey of Domain Adaptation for Neural Machine Translation (Chu & Wang, COLING 2018)

ACL

• Chenhui Chu and Rui Wang. 2018. A Survey of Domain Adaptation for Neural Machine Translation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1304–1319, Santa Fe, New Mexico, USA. Association for Computational Linguistics.