<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2019-empirical">
    <titleInfo>
        <title>An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zhisong</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xuezhe</namePart>
        <namePart type="family">Ma</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eduard</namePart>
        <namePart type="family">Hovy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.</abstract>
    <identifier type="citekey">zhang-etal-2019-empirical</identifier>
    <identifier type="doi">10.18653/v1/P19-1562</identifier>
    <location>
        <url>https://aclanthology.org/P19-1562/</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>5592</start>
            <end>5598</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing
%A Zhang, Zhisong
%A Ma, Xuezhe
%A Hovy, Eduard
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-etal-2019-empirical
%X In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.
%R 10.18653/v1/P19-1562
%U https://aclanthology.org/P19-1562/
%U https://doi.org/10.18653/v1/P19-1562
%P 5592-5598

Markdown (Informal)

[An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing](https://aclanthology.org/P19-1562/) (Zhang et al., ACL 2019)

• An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing (Zhang et al., ACL 2019)

ACL

• Zhisong Zhang, Xuezhe Ma, and Eduard Hovy. 2019. An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5592–5598, Florence, Italy. Association for Computational Linguistics.