<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2020-neural">
    <titleInfo>
        <title>Neural Dialogue State Tracking with Temporally Expressive Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Junfan</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Richong</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yongyi</namePart>
        <namePart type="family">Mao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jie</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2020</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.</abstract>
    <identifier type="citekey">chen-etal-2020-neural</identifier>
    <identifier type="doi">10.18653/v1/2020.findings-emnlp.142</identifier>
    <location>
        <url>https://aclanthology.org/2020.findings-emnlp.142</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>1570</start>
            <end>1579</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Dialogue State Tracking with Temporally Expressive Networks
%A Chen, Junfan
%A Zhang, Richong
%A Mao, Yongyi
%A Xu, Jie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-neural
%X Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.
%R 10.18653/v1/2020.findings-emnlp.142
%U https://aclanthology.org/2020.findings-emnlp.142
%U https://doi.org/10.18653/v1/2020.findings-emnlp.142
%P 1570-1579

Markdown (Informal)

[Neural Dialogue State Tracking with Temporally Expressive Networks](https://aclanthology.org/2020.findings-emnlp.142) (Chen et al., Findings 2020)

• Neural Dialogue State Tracking with Temporally Expressive Networks (Chen et al., Findings 2020)

ACL

• Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xu. 2020. Neural Dialogue State Tracking with Temporally Expressive Networks. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1570–1579, Online. Association for Computational Linguistics.