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        <title>Estimating User Interest from Open-Domain Dialogue</title>
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    <name type="personal">
        <namePart type="given">Michimasa</namePart>
        <namePart type="family">Inaba</namePart>
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    <name type="personal">
        <namePart type="given">Kenichi</namePart>
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            <title>Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue</title>
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        <name type="personal">
            <namePart type="given">Kazunori</namePart>
            <namePart type="family">Komatani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Diane</namePart>
            <namePart type="family">Litman</namePart>
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        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">Yu</namePart>
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            <namePart type="given">Alex</namePart>
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        <name type="personal">
            <namePart type="given">Lawrence</namePart>
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        <name type="personal">
            <namePart type="given">Mikio</namePart>
            <namePart type="family">Nakano</namePart>
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    <abstract>Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users’ interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users’ interests from their utterances in chat dialogues to personalize dialogue systems’ responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user’s interest more accurately than baseline approaches.</abstract>
    <identifier type="citekey">inaba-takahashi-2018-estimating</identifier>
    <identifier type="doi">10.18653/v1/W18-5004</identifier>
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        <url>https://aclanthology.org/W18-5004</url>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Estimating User Interest from Open-Domain Dialogue
%A Inaba, Michimasa
%A Takahashi, Kenichi
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F inaba-takahashi-2018-estimating
%X Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users’ interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users’ interests from their utterances in chat dialogues to personalize dialogue systems’ responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user’s interest more accurately than baseline approaches.
%R 10.18653/v1/W18-5004
%U https://aclanthology.org/W18-5004
%U https://doi.org/10.18653/v1/W18-5004
%P 32-40

Markdown (Informal)

[Estimating User Interest from Open-Domain Dialogue](https://aclanthology.org/W18-5004) (Inaba & Takahashi, SIGDIAL 2018)

• Estimating User Interest from Open-Domain Dialogue (Inaba & Takahashi, SIGDIAL 2018)

ACL

• Michimasa Inaba and Kenichi Takahashi. 2018. Estimating User Interest from Open-Domain Dialogue. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 32–40, Melbourne, Australia. Association for Computational Linguistics.