Natural language generation for social robotics: Opportunities and challenges

Foster, M. E. (2019) Natural language generation for social robotics: Opportunities and challenges. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1771), 20180027. (doi: 10.1098/rstb.2018.0027) (PMID:30853003) (PMCID:PMC6452247)

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In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social conversation is fluent, flexible linguistic interaction: as Bavelas et al. [1] point out, face-to-face dialogue is both the basic form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the field of computational linguistics devoted to the automated production of high-quality linguistic content: while this research area is also an active one, in general most effort in NLG is focussed on producing high-quality written text. This article summarises the state-of-the-art in the two individual research areas of social robotics and natural language generation. It then discusses the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate to the needs of socially interactive robots.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Foster, Dr Mary Ellen
Authors: Foster, M. E.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Philosophical Transactions of the Royal Society B: Biological Sciences
Publisher:The Royal Society
ISSN (Online):1471-2970
Published Online:11 March 2019
Copyright Holders:Copyright © 2019 The Author
First Published:First published in Philosophical Transactions of the Royal Society B: Biological Sciences 374(1771): 20180027
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
701651MUMMERMary Ellen FosterEuropean Commission (EC)688147SCHOOL OF COMPUTING SCIENCE