Detecting Agreement in Multi-party Conversational AI

Laura Schauer; Jason Sweeney; Charlie Lyttle; Zein Said; Aron Szeles; Cale Clark; Katie McAskill; Xander Wickham; Tom Byars; Daniel Hernández Garcia; Nancie Gunson; Angus Addlesee; Oliver Lemon

arXiv · 2311.03026

Detecting Agreement in Multi-party Conversational AI

Laura Schauer·Jason Sweeney·Charlie Lyttle·Zein Said·Aron Szeles·Cale Clark·Katie McAskill·Xander Wickham·Tom Byars·Daniel Hernández Garcia·Nancie Gunson·Angus Addlesee·Oliver Lemon

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Abstract

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.

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Laura Schauer, Jason Sweeney, Charlie Lyttle, Zein Said, Aron Szeles, Cale Clark, Katie McAskill, Xander Wickham, Tom Byars, Daniel Hernández Garcia, Nancie Gunson, Angus Addlesee, Oliver Lemon. 2023-11-06. Detecting Agreement in Multi-party Conversational AI. https://arxiv.org/abs/2311.03026

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