Today, conversational systems are expected to handle conversations in multi-party settings, especially within Socially Assistive Robots (SARs). However, practical usability remains difficult as there are additional challenges to overcome, such as speaker recognition, addressee recognition, and complex turn-taking. In this paper, we present our work on a multi-party conversational system, which invites two users to play a trivia quiz game. The system detects users' agreement or disagreement on a final answer and responds accordingly. Our evaluation includes both performance and user assessment results, with a focus on detecting user agreement. Our annotated transcripts and the code for the proposed system have been released open-source on GitHub.
@article{arxiv.2311.03026,
title = {Detecting Agreement in Multi-party Conversational AI},
author = {Laura Schauer and Jason Sweeney and Charlie Lyttle and Zein Said and Aron Szeles and Cale Clark and Katie McAskill and Xander Wickham and Tom Byars and Daniel Hernández Garcia and Nancie Gunson and Angus Addlesee and Oliver Lemon},
journal= {arXiv preprint arXiv:2311.03026},
year = {2023}
}
Comments
Proceedings of the workshop on advancing GROup UNderstanding and robots aDaptive behaviour (GROUND), 2023