<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="he-etal-2020-syntactic">
    <titleInfo>
        <title>Syntactic Graph Convolutional Network for Spoken Language Understanding</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Keqing</namePart>
        <namePart type="family">He</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuyu</namePart>
        <namePart type="family">Lei</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yushu</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Huixing</namePart>
        <namePart type="family">Jiang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhongyuan</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.</abstract>
    <identifier type="citekey">he-etal-2020-syntactic</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.246</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.246/</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>2728</start>
            <end>2738</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Syntactic Graph Convolutional Network for Spoken Language Understanding
%A He, Keqing
%A Lei, Shuyu
%A Yang, Yushu
%A Jiang, Huixing
%A Wang, Zhongyuan
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F he-etal-2020-syntactic
%X Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.
%R 10.18653/v1/2020.coling-main.246
%U https://aclanthology.org/2020.coling-main.246/
%U https://doi.org/10.18653/v1/2020.coling-main.246
%P 2728-2738

Markdown (Informal)

[Syntactic Graph Convolutional Network for Spoken Language Understanding](https://aclanthology.org/2020.coling-main.246/) (He et al., COLING 2020)

• Syntactic Graph Convolutional Network for Spoken Language Understanding (He et al., COLING 2020)

ACL

• Keqing He, Shuyu Lei, Yushu Yang, Huixing Jiang, and Zhongyuan Wang. 2020. Syntactic Graph Convolutional Network for Spoken Language Understanding. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2728–2738, Barcelona, Spain (Online). International Committee on Computational Linguistics.