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        <title>Document Context Neural Machine Translation with Memory Networks</title>
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    <name type="personal">
        <namePart type="given">Sameen</namePart>
        <namePart type="family">Maruf</namePart>
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    <name type="personal">
        <namePart type="given">Gholamreza</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>We present a document-level neural machine translation model which takes both source and target document context into account using memory networks. We model the problem as a structured prediction problem with interdependencies among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.</abstract>
    <identifier type="citekey">maruf-haffari-2018-document</identifier>
    <identifier type="doi">10.18653/v1/P18-1118</identifier>
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        <url>https://aclanthology.org/P18-1118</url>
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    <part>
        <date>2018-07</date>
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            <start>1275</start>
            <end>1284</end>
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%0 Conference Proceedings
%T Document Context Neural Machine Translation with Memory Networks
%A Maruf, Sameen
%A Haffari, Gholamreza
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F maruf-haffari-2018-document
%X We present a document-level neural machine translation model which takes both source and target document context into account using memory networks. We model the problem as a structured prediction problem with interdependencies among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.
%R 10.18653/v1/P18-1118
%U https://aclanthology.org/P18-1118
%U https://doi.org/10.18653/v1/P18-1118
%P 1275-1284

Markdown (Informal)

[Document Context Neural Machine Translation with Memory Networks](https://aclanthology.org/P18-1118) (Maruf & Haffari, ACL 2018)

• Document Context Neural Machine Translation with Memory Networks (Maruf & Haffari, ACL 2018)

ACL

• Sameen Maruf and Gholamreza Haffari. 2018. Document Context Neural Machine Translation with Memory Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1275–1284, Melbourne, Australia. Association for Computational Linguistics.