<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2020-listeners">
    <titleInfo>
        <title>Listener’s Social Identity Matters in Personalised Response Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Guanyi</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yinhe</namePart>
        <namePart type="family">Zheng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yupei</namePart>
        <namePart type="family">Du</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 13th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Brian</namePart>
            <namePart type="family">Davis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yvette</namePart>
            <namePart type="family">Graham</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">John</namePart>
            <namePart type="family">Kelleher</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yaji</namePart>
            <namePart type="family">Sripada</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener’s social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener’s identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener’s identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener’s identity, the personalised response generator performs better in its own identity.</abstract>
    <identifier type="citekey">chen-etal-2020-listeners</identifier>
    <identifier type="doi">10.18653/v1/2020.inlg-1.26</identifier>
    <location>
        <url>https://aclanthology.org/2020.inlg-1.26</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>205</start>
            <end>215</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Listener’s Social Identity Matters in Personalised Response Generation
%A Chen, Guanyi
%A Zheng, Yinhe
%A Du, Yupei
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F chen-etal-2020-listeners
%X Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener’s social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener’s identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener’s identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener’s identity, the personalised response generator performs better in its own identity.
%R 10.18653/v1/2020.inlg-1.26
%U https://aclanthology.org/2020.inlg-1.26
%U https://doi.org/10.18653/v1/2020.inlg-1.26
%P 205-215

Markdown (Informal)

[Listener’s Social Identity Matters in Personalised Response Generation](https://aclanthology.org/2020.inlg-1.26) (Chen et al., INLG 2020)

• Listener’s Social Identity Matters in Personalised Response Generation (Chen et al., INLG 2020)

ACL

• Guanyi Chen, Yinhe Zheng, and Yupei Du. 2020. Listener’s Social Identity Matters in Personalised Response Generation. In Proceedings of the 13th International Conference on Natural Language Generation, pages 205–215, Dublin, Ireland. Association for Computational Linguistics.