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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5013.pdf