<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="long-etal-2017-patent">
    <titleInfo>
        <title>Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zi</namePart>
        <namePart type="family">Long</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ryuichiro</namePart>
        <namePart type="family">Kimura</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Takehito</namePart>
        <namePart type="family">Utsuro</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tomoharu</namePart>
        <namePart type="family">Mitsuhashi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mikio</namePart>
        <namePart type="family">Yamamoto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 4th Workshop on Asian Translation (WAT2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Toshiaki</namePart>
            <namePart type="family">Nakazawa</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isao</namePart>
            <namePart type="family">Goto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Asian Federation of Natural Language Processing</publisher>
            <place>
                <placeTerm type="text">Taipei, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).</abstract>
    <identifier type="citekey">long-etal-2017-patent</identifier>
    <location>
        <url>https://aclanthology.org/W17-5709</url>
    </location>
    <part>
        <date>2017-11</date>
        <extent unit="page">
            <start>110</start>
            <end>118</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017
%A Long, Zi
%A Kimura, Ryuichiro
%A Utsuro, Takehito
%A Mitsuhashi, Tomoharu
%A Yamamoto, Mikio
%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 long-etal-2017-patent
%X Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).
%U https://aclanthology.org/W17-5709
%P 110-118

Markdown (Informal)

[Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017](https://aclanthology.org/W17-5709) (Long et al., WAT 2017)

• Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017 (Long et al., WAT 2017)

ACL

• Zi Long, Ryuichiro Kimura, Takehito Utsuro, Tomoharu Mitsuhashi, and Mikio Yamamoto. 2017. Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 110–118, Taipei, Taiwan. Asian Federation of Natural Language Processing.