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        <title>Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016</title>
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        <namePart type="given">Graham</namePart>
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            <title>Proceedings of the 3rd Workshop on Asian Translation (WAT2016)</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">Hideya</namePart>
            <namePart type="family">Mino</namePart>
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        <name type="personal">
            <namePart type="given">Chenchen</namePart>
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            <namePart type="family">Kurohashi</namePart>
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            <namePart type="given">Ir.</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task.</abstract>
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%0 Conference Proceedings
%T Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016
%A Neubig, Graham
%Y Nakazawa, Toshiaki
%Y Mino, Hideya
%Y Ding, Chenchen
%Y Goto, Isao
%Y Neubig, Graham
%Y Kurohashi, Sadao
%Y Riza, Ir. Hammam
%Y Bhattacharyya, Pushpak
%S Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F neubig-2016-lexicons
%X This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task.
%U https://aclanthology.org/W16-4610
%P 119-125

Markdown (Informal)

[Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016](https://aclanthology.org/W16-4610) (Neubig, WAT 2016)

• Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016 (Neubig, WAT 2016)

ACL

• Graham Neubig. 2016. Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 119–125, Osaka, Japan. The COLING 2016 Organizing Committee.