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
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.