<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="he-etal-2018-exploring">
    <titleInfo>
        <title>Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xuanli</namePart>
        <namePart type="family">He</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Quan</namePart>
        <namePart type="family">Tran</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">William</namePart>
        <namePart type="family">Havard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Laurent</namePart>
        <namePart type="family">Besacier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ingrid</namePart>
        <namePart type="family">Zukerman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gholamreza</namePart>
        <namePart type="family">Haffari</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Australasian Language Technology Association Workshop 2018</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sunghwan</namePart>
            <namePart type="given">Mac</namePart>
            <namePart type="family">Kim</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xiuzhen</namePart>
            <namePart type="given">(Jenny)</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
                <placeTerm type="text">Dunedin, New Zealand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)‘s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.</abstract>
    <identifier type="citekey">he-etal-2018-exploring</identifier>
    <location>
        <url>https://aclanthology.org/U18-1007/</url>
    </location>
    <part>
        <date>2018-12</date>
        <extent unit="page">
            <start>61</start>
            <end>65</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
%A He, Xuanli
%A Tran, Quan
%A Havard, William
%A Besacier, Laurent
%A Zukerman, Ingrid
%A Haffari, Gholamreza
%Y Kim, Sunghwan Mac
%Y Zhang, Xiuzhen (Jenny)
%S Proceedings of the Australasian Language Technology Association Workshop 2018
%D 2018
%8 December
%C Dunedin, New Zealand
%F he-etal-2018-exploring
%X In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR)‘s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.
%U https://aclanthology.org/U18-1007/
%P 61-65

Markdown (Informal)

[Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation](https://aclanthology.org/U18-1007/) (He et al., ALTA 2018)

• Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation (He et al., ALTA 2018)

ACL

• Xuanli He, Quan Tran, William Havard, Laurent Besacier, Ingrid Zukerman, and Gholamreza Haffari. 2018. Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation. In Proceedings of the Australasian Language Technology Association Workshop 2018, pages 61–65, Dunedin, New Zealand.