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        <title>Sentence Weighting for Neural Machine Translation Domain Adaptation</title>
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        <namePart type="given">Shiqi</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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    <abstract>In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation. We introduce a domain similarity metric to evaluate the relevance between a sentence and an available entire domain dataset. The similarity of each sentence to the target domain is calculated with various methods. The computed similarity is then integrated into the training objective to weight sentences. The adaptation results on both IWSLT Chinese-English TED task and a task with only synthetic training parallel data show that our sentence weighting method is able to achieve an significant improvement over strong baselines.</abstract>
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%0 Conference Proceedings
%T Sentence Weighting for Neural Machine Translation Domain Adaptation
%A Zhang, Shiqi
%A Xiong, Deyi
%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 zhang-xiong-2018-sentence
%X In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation. We introduce a domain similarity metric to evaluate the relevance between a sentence and an available entire domain dataset. The similarity of each sentence to the target domain is calculated with various methods. The computed similarity is then integrated into the training objective to weight sentences. The adaptation results on both IWSLT Chinese-English TED task and a task with only synthetic training parallel data show that our sentence weighting method is able to achieve an significant improvement over strong baselines.
%U https://aclanthology.org/C18-1269
%P 3181-3190

Markdown (Informal)

[Sentence Weighting for Neural Machine Translation Domain Adaptation](https://aclanthology.org/C18-1269) (Zhang & Xiong, COLING 2018)

• Sentence Weighting for Neural Machine Translation Domain Adaptation (Zhang & Xiong, COLING 2018)

ACL

• Shiqi Zhang and Deyi Xiong. 2018. Sentence Weighting for Neural Machine Translation Domain Adaptation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3181–3190, Santa Fe, New Mexico, USA. Association for Computational Linguistics.