<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yamagishi-etal-2017-improving">
    <titleInfo>
        <title>Improving Japanese-to-English Neural Machine Translation by Voice Prediction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hayahide</namePart>
        <namePart type="family">Yamagishi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shin</namePart>
        <namePart type="family">Kanouchi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Takayuki</namePart>
        <namePart type="family">Sato</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mamoru</namePart>
        <namePart type="family">Komachi</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 Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Taro</namePart>
            <namePart type="family">Watanabe</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>This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences. Our previous study presented a voice controlling method to generate sentences for neural machine translation, wherein it was demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that of the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correct translation in advance. Thus, this study presents a voice prediction method for generated sentences for neural machine translation. While evaluating on Japanese-to-English translation, we obtain a 0.70-improvement in the BLEU using the predicted voice.</abstract>
    <identifier type="citekey">yamagishi-etal-2017-improving</identifier>
    <location>
        <url>https://aclanthology.org/I17-2047</url>
    </location>
    <part>
        <date>2017-11</date>
        <extent unit="page">
            <start>277</start>
            <end>282</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Japanese-to-English Neural Machine Translation by Voice Prediction
%A Yamagishi, Hayahide
%A Kanouchi, Shin
%A Sato, Takayuki
%A Komachi, Mamoru
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F yamagishi-etal-2017-improving
%X This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences. Our previous study presented a voice controlling method to generate sentences for neural machine translation, wherein it was demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that of the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correct translation in advance. Thus, this study presents a voice prediction method for generated sentences for neural machine translation. While evaluating on Japanese-to-English translation, we obtain a 0.70-improvement in the BLEU using the predicted voice.
%U https://aclanthology.org/I17-2047
%P 277-282

Markdown (Informal)

[Improving Japanese-to-English Neural Machine Translation by Voice Prediction](https://aclanthology.org/I17-2047) (Yamagishi et al., IJCNLP 2017)

• Improving Japanese-to-English Neural Machine Translation by Voice Prediction (Yamagishi et al., IJCNLP 2017)

ACL

• Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi. 2017. Improving Japanese-to-English Neural Machine Translation by Voice Prediction. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 277–282, Taipei, Taiwan. Asian Federation of Natural Language Processing.