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 García, Nancie Gunson, Angus Addlesee, Oliver Lemon

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.