An Empirical Study of Self-Disclosure in Spoken Dialogue Systems

Abhilasha Ravichander, Alan W. Black


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.
Anthology ID:
W18-5030
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
253–263
Language:
URL:
https://aclanthology.org/W18-5030
DOI:
10.18653/v1/W18-5030
Bibkey:
Cite (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.
Cite (Informal):
An Empirical Study of Self-Disclosure in Spoken Dialogue Systems (Ravichander & Black, SIGDIAL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5030.pdf