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        <title>An Empirical Study of Self-Disclosure in Spoken Dialogue Systems</title>
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    <name type="personal">
        <namePart type="given">Abhilasha</namePart>
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            <title>Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue</title>
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            <namePart type="given">Kazunori</namePart>
            <namePart type="family">Komatani</namePart>
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        <name type="personal">
            <namePart type="given">Diane</namePart>
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            <namePart type="given">Kai</namePart>
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            <namePart type="given">Lawrence</namePart>
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    <abstract>Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5030</identifier>
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%0 Conference Proceedings
%T An Empirical Study of Self-Disclosure in Spoken Dialogue Systems
%A Ravichander, Abhilasha
%A Black, Alan W.
%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 ravichander-black-2018-empirical
%X Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.
%R 10.18653/v1/W18-5030
%U https://aclanthology.org/W18-5030
%U https://doi.org/10.18653/v1/W18-5030
%P 253-263

Markdown (Informal)

[An Empirical Study of Self-Disclosure in Spoken Dialogue Systems](https://aclanthology.org/W18-5030) (Ravichander & Black, SIGDIAL 2018)

• An Empirical Study of Self-Disclosure in Spoken Dialogue Systems (Ravichander & Black, SIGDIAL 2018)

ACL

• Abhilasha Ravichander and Alan W. Black. 2018. An Empirical Study of Self-Disclosure in Spoken Dialogue Systems. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 253–263, Melbourne, Australia. Association for Computational Linguistics.