<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mendonca-etal-2022-qualityadapt">
    <titleInfo>
        <title>QualityAdapt: an Automatic Dialogue Quality Estimation Framework</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">John</namePart>
        <namePart type="family">Mendonca</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alon</namePart>
        <namePart type="family">Lavie</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Isabel</namePart>
        <namePart type="family">Trancoso</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Oliver</namePart>
            <namePart type="family">Lemon</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">Junyi</namePart>
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Arash</namePart>
            <namePart type="family">Ashrafzadeh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="given">Hernández</namePart>
            <namePart type="family">Garcia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Malihe</namePart>
            <namePart type="family">Alikhani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Vandyke</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ondřej</namePart>
            <namePart type="family">Dušek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Edinburgh, UK</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck. Several automated metrics have been proposed to evaluate these systems, however, they mostly focus on a single notion of quality, or, when they do combine several sub-metrics, they are computationally expensive. This paper attempts to solve the latter: QualityAdapt leverages the Adapter framework for the task of Dialogue Quality Estimation. Using well defined semi-supervised tasks, we train adapters for different subqualities and score generated responses with AdapterFusion. This compositionality provides an easy to adapt metric to the task at hand that incorporates multiple subqualities. It also reduces computational costs as individual predictions of all subqualities are obtained in a single forward pass. This approach achieves comparable results to state-of-the-art metrics on several datasets, whilst keeping the previously mentioned advantages.</abstract>
    <identifier type="citekey">mendonca-etal-2022-qualityadapt</identifier>
    <identifier type="doi">10.18653/v1/2022.sigdial-1.9</identifier>
    <location>
        <url>https://aclanthology.org/2022.sigdial-1.9</url>
    </location>
    <part>
        <date>2022-09</date>
        <extent unit="page">
            <start>83</start>
            <end>90</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T QualityAdapt: an Automatic Dialogue Quality Estimation Framework
%A Mendonca, John
%A Lavie, Alon
%A Trancoso, Isabel
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F mendonca-etal-2022-qualityadapt
%X Despite considerable advances in open-domain neural dialogue systems, their evaluation remains a bottleneck. Several automated metrics have been proposed to evaluate these systems, however, they mostly focus on a single notion of quality, or, when they do combine several sub-metrics, they are computationally expensive. This paper attempts to solve the latter: QualityAdapt leverages the Adapter framework for the task of Dialogue Quality Estimation. Using well defined semi-supervised tasks, we train adapters for different subqualities and score generated responses with AdapterFusion. This compositionality provides an easy to adapt metric to the task at hand that incorporates multiple subqualities. It also reduces computational costs as individual predictions of all subqualities are obtained in a single forward pass. This approach achieves comparable results to state-of-the-art metrics on several datasets, whilst keeping the previously mentioned advantages.
%R 10.18653/v1/2022.sigdial-1.9
%U https://aclanthology.org/2022.sigdial-1.9
%U https://doi.org/10.18653/v1/2022.sigdial-1.9
%P 83-90

Markdown (Informal)

[QualityAdapt: an Automatic Dialogue Quality Estimation Framework](https://aclanthology.org/2022.sigdial-1.9) (Mendonca et al., SIGDIAL 2022)

• QualityAdapt: an Automatic Dialogue Quality Estimation Framework (Mendonca et al., SIGDIAL 2022)

ACL

• John Mendonca, Alon Lavie, and Isabel Trancoso. 2022. QualityAdapt: an Automatic Dialogue Quality Estimation Framework. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 83–90, Edinburgh, UK. Association for Computational Linguistics.