<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="feng-etal-2016-english">
    <titleInfo>
        <title>English-Chinese Knowledge Base Translation with Neural Network</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xiaocheng</namePart>
        <namePart type="family">Feng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Duyu</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bing</namePart>
        <namePart type="family">Qin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ting</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The COLING 2016 Organizing Committee</publisher>
            <place>
                <placeTerm type="text">Osaka, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.</abstract>
    <identifier type="citekey">feng-etal-2016-english</identifier>
    <location>
        <url>https://aclanthology.org/C16-1276</url>
    </location>
    <part>
        <date>2016-12</date>
        <extent unit="page">
            <start>2935</start>
            <end>2944</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T English-Chinese Knowledge Base Translation with Neural Network
%A Feng, Xiaocheng
%A Tang, Duyu
%A Qin, Bing
%A Liu, Ting
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F feng-etal-2016-english
%X Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.
%U https://aclanthology.org/C16-1276
%P 2935-2944

Markdown (Informal)

[English-Chinese Knowledge Base Translation with Neural Network](https://aclanthology.org/C16-1276) (Feng et al., COLING 2016)

• English-Chinese Knowledge Base Translation with Neural Network (Feng et al., COLING 2016)

ACL

• Xiaocheng Feng, Duyu Tang, Bing Qin, and Ting Liu. 2016. English-Chinese Knowledge Base Translation with Neural Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2935–2944, Osaka, Japan. The COLING 2016 Organizing Committee.