@inproceedings{das-pon-barry-2018-turn,
title = "Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue",
author = "Das, Ranjini and
Pon-Barry, Heather",
editor = "Komatani, Kazunori and
Litman, Diane and
Yu, Kai and
Papangelis, Alex and
Cavedon, Lawrence and
Nakano, Mikio",
booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5013",
doi = "10.18653/v1/W18-5013",
pages = "119--129",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue
%A Das, Ranjini
%A Pon-Barry, Heather
%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 das-pon-barry-2018-turn
%X 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.
%R 10.18653/v1/W18-5013
%U https://aclanthology.org/W18-5013
%U https://doi.org/10.18653/v1/W18-5013
%P 119-129
Markdown (Informal)
[Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue](https://aclanthology.org/W18-5013) (Das & Pon-Barry, SIGDIAL 2018)
ACL