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        <title>Context-aware Neural Machine Translation with Coreference Information</title>
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    <name type="personal">
        <namePart type="given">Takumi</namePart>
        <namePart type="family">Ohtani</namePart>
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        <namePart type="given">Hidetaka</namePart>
        <namePart type="family">Kamigaito</namePart>
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    <name type="personal">
        <namePart type="given">Masaaki</namePart>
        <namePart type="family">Nagata</namePart>
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    <name type="personal">
        <namePart type="given">Manabu</namePart>
        <namePart type="family">Okumura</namePart>
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            <title>Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)</title>
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            <namePart type="given">Andrei</namePart>
            <namePart type="family">Popescu-Belis</namePart>
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            <namePart type="given">Christian</namePart>
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    <abstract>We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.</abstract>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Context-aware Neural Machine Translation with Coreference Information
%A Ohtani, Takumi
%A Kamigaito, Hidetaka
%A Nagata, Masaaki
%A Okumura, Manabu
%Y Popescu-Belis, Andrei
%Y Loáiciga, Sharid
%Y Hardmeier, Christian
%Y Xiong, Deyi
%S Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ohtani-etal-2019-context
%X We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.
%R 10.18653/v1/D19-6505
%U https://aclanthology.org/D19-6505
%U https://doi.org/10.18653/v1/D19-6505
%P 45-50

Markdown (Informal)

[Context-aware Neural Machine Translation with Coreference Information](https://aclanthology.org/D19-6505) (Ohtani et al., DiscoMT 2019)

• Context-aware Neural Machine Translation with Coreference Information (Ohtani et al., DiscoMT 2019)

ACL

• Takumi Ohtani, Hidetaka Kamigaito, Masaaki Nagata, and Manabu Okumura. 2019. Context-aware Neural Machine Translation with Coreference Information. In Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019), pages 45–50, Hong Kong, China. Association for Computational Linguistics.