@inproceedings{mcneill-kennington-2020-learning,
title = "Learning Word Groundings from Humans Facilitated by Robot Emotional Displays",
author = "McNeill, David and
Kennington, Casey",
editor = "Pietquin, Olivier and
Muresan, Smaranda and
Chen, Vivian and
Kennington, Casey and
Vandyke, David and
Dethlefs, Nina and
Inoue, Koji and
Ekstedt, Erik and
Ultes, Stefan",
booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2020",
address = "1st virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigdial-1.13",
doi = "10.18653/v1/2020.sigdial-1.13",
pages = "97--106",
abstract = "In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not{--}indeed, cannot{--}yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.",
}
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<abstract>In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.</abstract>
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%0 Conference Proceedings
%T Learning Word Groundings from Humans Facilitated by Robot Emotional Displays
%A McNeill, David
%A Kennington, Casey
%Y Pietquin, Olivier
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Kennington, Casey
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Inoue, Koji
%Y Ekstedt, Erik
%Y Ultes, Stefan
%S Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2020
%8 July
%I Association for Computational Linguistics
%C 1st virtual meeting
%F mcneill-kennington-2020-learning
%X In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.
%R 10.18653/v1/2020.sigdial-1.13
%U https://aclanthology.org/2020.sigdial-1.13
%U https://doi.org/10.18653/v1/2020.sigdial-1.13
%P 97-106
Markdown (Informal)
[Learning Word Groundings from Humans Facilitated by Robot Emotional Displays](https://aclanthology.org/2020.sigdial-1.13) (McNeill & Kennington, SIGDIAL 2020)
ACL