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        <title>Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017</title>
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    <name type="personal">
        <namePart type="given">Satoshi</namePart>
        <namePart type="family">Kinoshita</namePart>
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        <namePart type="given">Tadaaki</namePart>
        <namePart type="family">Oshio</namePart>
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    <name type="personal">
        <namePart type="given">Tomoharu</namePart>
        <namePart type="family">Mitsuhashi</namePart>
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            <title>Proceedings of the 4th Workshop on Asian Translation (WAT2017)</title>
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            <namePart type="given">Toshiaki</namePart>
            <namePart type="family">Nakazawa</namePart>
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        <name type="personal">
            <namePart type="given">Isao</namePart>
            <namePart type="family">Goto</namePart>
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            <publisher>Asian Federation of Natural Language Processing</publisher>
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    <abstract>Japio participates in patent subtasks (JPC-EJ/JE/CJ/KJ) with phrase-based statistical machine translation (SMT) and neural machine translation (NMT) systems which are trained with its own patent corpora in addition to the subtask corpora provided by organizers of WAT2017. In EJ and CJ subtasks, SMT and NMT systems whose sizes of training corpora are about 50 million and 10 million sentence pairs respectively achieved comparable scores for automatic evaluations, but NMT systems were superior to SMT systems for both official and in-house human evaluations.</abstract>
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        <url>https://aclanthology.org/W17-5713</url>
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        <date>2017-11</date>
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%0 Conference Proceedings
%T Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017
%A Kinoshita, Satoshi
%A Oshio, Tadaaki
%A Mitsuhashi, Tomoharu
%Y Nakazawa, Toshiaki
%Y Goto, Isao
%S Proceedings of the 4th Workshop on Asian Translation (WAT2017)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F kinoshita-etal-2017-comparison
%X Japio participates in patent subtasks (JPC-EJ/JE/CJ/KJ) with phrase-based statistical machine translation (SMT) and neural machine translation (NMT) systems which are trained with its own patent corpora in addition to the subtask corpora provided by organizers of WAT2017. In EJ and CJ subtasks, SMT and NMT systems whose sizes of training corpora are about 50 million and 10 million sentence pairs respectively achieved comparable scores for automatic evaluations, but NMT systems were superior to SMT systems for both official and in-house human evaluations.
%U https://aclanthology.org/W17-5713
%P 140-145

Markdown (Informal)

[Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017](https://aclanthology.org/W17-5713) (Kinoshita et al., WAT 2017)

• Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017 (Kinoshita et al., WAT 2017)

ACL

• Satoshi Kinoshita, Tadaaki Oshio, and Tomoharu Mitsuhashi. 2017. Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 140–145, Taipei, Taiwan. Asian Federation of Natural Language Processing.