<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2019-second">
    <titleInfo>
        <title>Second-Order Semantic Dependency Parsing with End-to-End Neural Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xinyu</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jingxian</namePart>
        <namePart type="family">Huang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kewei</namePart>
        <namePart type="family">Tu</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>Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.</abstract>
    <identifier type="citekey">wang-etal-2019-second</identifier>
    <identifier type="doi">10.18653/v1/P19-1454</identifier>
    <location>
        <url>https://aclanthology.org/P19-1454/</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>4609</start>
            <end>4618</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
%A Wang, Xinyu
%A Huang, Jingxian
%A Tu, Kewei
%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 wang-etal-2019-second
%X Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.
%R 10.18653/v1/P19-1454
%U https://aclanthology.org/P19-1454/
%U https://doi.org/10.18653/v1/P19-1454
%P 4609-4618

Markdown (Informal)

[Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://aclanthology.org/P19-1454/) (Wang et al., ACL 2019)

• Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (Wang et al., ACL 2019)

ACL

• Xinyu Wang, Jingxian Huang, and Kewei Tu. 2019. Second-Order Semantic Dependency Parsing with End-to-End Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4609–4618, Florence, Italy. Association for Computational Linguistics.