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        <title>Sequence-to-Dependency Neural Machine Translation</title>
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        <namePart type="given">Shuangzhi</namePart>
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    <name type="personal">
        <namePart type="given">Ming</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.</abstract>
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%0 Conference Proceedings
%T Sequence-to-Dependency Neural Machine Translation
%A Wu, Shuangzhi
%A Zhang, Dongdong
%A Yang, Nan
%A Li, Mu
%A Zhou, Ming
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wu-etal-2017-sequence
%X Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.
%R 10.18653/v1/P17-1065
%U https://aclanthology.org/P17-1065
%U https://doi.org/10.18653/v1/P17-1065
%P 698-707

Markdown (Informal)

[Sequence-to-Dependency Neural Machine Translation](https://aclanthology.org/P17-1065) (Wu et al., ACL 2017)

• Sequence-to-Dependency Neural Machine Translation (Wu et al., ACL 2017)

ACL

• Shuangzhi Wu, Dongdong Zhang, Nan Yang, Mu Li, and Ming Zhou. 2017. Sequence-to-Dependency Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 698–707, Vancouver, Canada. Association for Computational Linguistics.