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Conversational agents participating in multi-party interactions facesignificant challenges in dialogue state tracking, since the identity of thespeaker adds significant contextual meaning. It is common to utilisediarisation models to identify the speaker. However, it is not clear if theseare accurate enough to correctly identify specific conversational events suchas agreement or disagreement during a real-time interaction. This study uses acooperative quiz, where the conversational agent acts as quiz-show host, todetermine whether diarisation or a frequency-and-proximity-based method is moreaccurate at determining agreement, and whether this translates to feelings ofengagement from the players. Experimental results show that our proceduralsystem was more engaging to players, and was more accurate at detectingagreement, reaching an average accuracy of 0.44 compared to 0.28 for thediarised system.

Detecting agreement in multi-party dialogue: evaluating speaker diarisation versus a procedural baseline to enhance user engagement