Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue

Ranjini Das, Heather Pon-Barry


Abstract
In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue.
Anthology ID:
W18-5013
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:
119–129
Language:
URL:
https://aclanthology.org/W18-5013
DOI:
10.18653/v1/W18-5013
Bibkey:
Cite (ACL):
Ranjini Das and Heather Pon-Barry. 2018. Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 119–129, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue (Das & Pon-Barry, SIGDIAL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5013.pdf