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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5030.pdf