<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="trinh-etal-2019-energy">
    <titleInfo>
        <title>Energy-Based Modelling for Dialogue State Tracking</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Anh</namePart>
        <namePart type="given">Duong</namePart>
        <namePart type="family">Trinh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="family">Ross</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">John</namePart>
        <namePart type="family">Kelleher</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on NLP for Conversational AI</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tania</namePart>
            <namePart type="family">Bedrax-Weiss</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dilek</namePart>
            <namePart type="family">Hakkani-Tur</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anuj</namePart>
            <namePart type="family">Kumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mike</namePart>
            <namePart type="family">Lewis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thang-Minh</namePart>
            <namePart type="family">Luong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pei-Hao</namePart>
            <namePart type="family">Su</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tsung-Hsien</namePart>
            <namePart type="family">Wen</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>The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.</abstract>
    <identifier type="citekey">trinh-etal-2019-energy</identifier>
    <identifier type="doi">10.18653/v1/W19-4109</identifier>
    <location>
        <url>https://aclanthology.org/W19-4109</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>77</start>
            <end>86</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Energy-Based Modelling for Dialogue State Tracking
%A Trinh, Anh Duong
%A Ross, Robert
%A Kelleher, John
%Y Chen, Yun-Nung
%Y Bedrax-Weiss, Tania
%Y Hakkani-Tur, Dilek
%Y Kumar, Anuj
%Y Lewis, Mike
%Y Luong, Thang-Minh
%Y Su, Pei-Hao
%Y Wen, Tsung-Hsien
%S Proceedings of the First Workshop on NLP for Conversational AI
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F trinh-etal-2019-energy
%X The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.
%R 10.18653/v1/W19-4109
%U https://aclanthology.org/W19-4109
%U https://doi.org/10.18653/v1/W19-4109
%P 77-86

Markdown (Informal)

[Energy-Based Modelling for Dialogue State Tracking](https://aclanthology.org/W19-4109) (Trinh et al., ACL 2019)

• Energy-Based Modelling for Dialogue State Tracking (Trinh et al., ACL 2019)

ACL

• Anh Duong Trinh, Robert Ross, and John Kelleher. 2019. Energy-Based Modelling for Dialogue State Tracking. In Proceedings of the First Workshop on NLP for Conversational AI, pages 77–86, Florence, Italy. Association for Computational Linguistics.