<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2017-mt">
    <titleInfo>
        <title>MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sheng</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Duh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Benjamin</namePart>
        <namePart type="family">Van Durme</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Phil</namePart>
            <namePart type="family">Blunsom</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexander</namePart>
            <namePart type="family">Koller</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Valencia, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.</abstract>
    <identifier type="citekey">zhang-etal-2017-mt</identifier>
    <location>
        <url>https://aclanthology.org/E17-2011</url>
    </location>
    <part>
        <date>2017-04</date>
        <extent unit="page">
            <start>64</start>
            <end>70</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models
%A Zhang, Sheng
%A Duh, Kevin
%A Van Durme, Benjamin
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F zhang-etal-2017-mt
%X Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.
%U https://aclanthology.org/E17-2011
%P 64-70

Markdown (Informal)

[MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models](https://aclanthology.org/E17-2011) (Zhang et al., EACL 2017)

• MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models (Zhang et al., EACL 2017)

ACL

• Sheng Zhang, Kevin Duh, and Benjamin Van Durme. 2017. MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 64–70, Valencia, Spain. Association for Computational Linguistics.