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        <title>Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language</title>
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    <name type="personal">
        <namePart type="given">Yuuki</namePart>
        <namePart type="family">Sekizawa</namePart>
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    <name type="personal">
        <namePart type="given">Tomoyuki</namePart>
        <namePart type="family">Kajiwara</namePart>
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    <name type="personal">
        <namePart type="given">Mamoru</namePart>
        <namePart type="family">Komachi</namePart>
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            <title>Proceedings of the 4th Workshop on Asian Translation (WAT2017)</title>
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        <name type="personal">
            <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>Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT). However, NMT has a very high computational cost because of the high dimensionality of the output layer. Generally, NMT restricts the size of vocabulary, which results in infrequent words being treated as out-of-vocabulary (OOV) and degrades the performance of the translation. In evaluation, we achieved a statistically significant BLEU score improvement of 0.55-0.77 over the baselines including the state-of-the-art method.</abstract>
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        <url>https://aclanthology.org/W17-5703</url>
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        <date>2017-11</date>
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%0 Conference Proceedings
%T Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language
%A Sekizawa, Yuuki
%A Kajiwara, Tomoyuki
%A Komachi, Mamoru
%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 sekizawa-etal-2017-improving
%X Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT). However, NMT has a very high computational cost because of the high dimensionality of the output layer. Generally, NMT restricts the size of vocabulary, which results in infrequent words being treated as out-of-vocabulary (OOV) and degrades the performance of the translation. In evaluation, we achieved a statistically significant BLEU score improvement of 0.55-0.77 over the baselines including the state-of-the-art method.
%U https://aclanthology.org/W17-5703
%P 64-69

Markdown (Informal)

[Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language](https://aclanthology.org/W17-5703) (Sekizawa et al., WAT 2017)

• Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language (Sekizawa et al., WAT 2017)

ACL

• Yuuki Sekizawa, Tomoyuki Kajiwara, and Mamoru Komachi. 2017. Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 64–69, Taipei, Taiwan. Asian Federation of Natural Language Processing.