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    <titleInfo>
        <title>Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs</title>
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    <name type="personal">
        <namePart type="given">Takashi</namePart>
        <namePart type="family">Kodama</namePart>
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    <name type="personal">
        <namePart type="given">Ryuichiro</namePart>
        <namePart type="family">Higashinaka</namePart>
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    <name type="personal">
        <namePart type="given">Koh</namePart>
        <namePart type="family">Mitsuda</namePart>
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    <name type="personal">
        <namePart type="given">Ryo</namePart>
        <namePart type="family">Masumura</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Yushi</namePart>
        <namePart type="family">Aono</namePart>
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    <name type="personal">
        <namePart type="given">Ryuta</namePart>
        <namePart type="family">Nakamura</namePart>
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    <name type="personal">
        <namePart type="given">Noritake</namePart>
        <namePart type="family">Adachi</namePart>
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    <name type="personal">
        <namePart type="given">Hidetoshi</namePart>
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            <title>Proceedings of the Twelfth Language Resources and Evaluation Conference</title>
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            <publisher>European Language Resources Association</publisher>
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    <abstract>This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.</abstract>
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%0 Conference Proceedings
%T Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs
%A Kodama, Takashi
%A Higashinaka, Ryuichiro
%A Mitsuda, Koh
%A Masumura, Ryo
%A Aono, Yushi
%A Nakamura, Ryuta
%A Adachi, Noritake
%A Kawabata, Hidetoshi
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F kodama-etal-2020-generating
%X This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.
%U https://aclanthology.org/2020.lrec-1.668
%P 5433-5441

Markdown (Informal)

[Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs](https://aclanthology.org/2020.lrec-1.668) (Kodama et al., LREC 2020)

• Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (Kodama et al., LREC 2020)

ACL

• Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, and Hidetoshi Kawabata. 2020. Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5433–5441, Marseille, France. European Language Resources Association.