多方对话中的一致性检测：评估说话人 diarisation 与程序基线以提升用户参与度
Detecting agreement in multi-party dialogue: evaluating speaker diarisation versus a procedural baseline to enhance user engagement
AI总结：
本研究针对多方对话中说话人身份影响对话状态跟踪的问题，对比说话人分群模型与基于频率邻近度的程序方法在一致性检测上的效果，发现后者准确率更高且更能提升用户参与度。
摘要：
参与多方互动的对话智能体在对话状态跟踪上面临重大挑战，因为说话人的身份承载着重要的语境意义。业界通常使用 diarisation（说话人分群）模型来识别说话人。但目前尚不清楚这些模型是否足够精准，能在实时互动中正确识别同意、反对这类特定对话事件。本研究采用合作问答游戏（对话智能体担任问答节目主持人），来判定 diarisation 方法和基于频率与邻近度的方法在识别一致性上哪个更准确，以及这种准确性是否会转化为玩家的参与感。实验结果表明，我们的程序系统对玩家来说更具参与感，且检测一致性的准确率更高，平均准确率达 0.44，而 diarisation 系统仅为 0.28。
英文摘要：
Conversational agents participating in multi-party interactions face significant challenges in dialogue state tracking, since the identity of the speaker adds significant contextual meaning. It is common to utilise diarisation models to identify the speaker. However, it is not clear if these are accurate enough to correctly identify specific conversational events such as agreement or disagreement during a real-time interaction. This study uses a cooperative quiz, where the conversational agent acts as quiz-show host, to determine whether diarisation or a frequency-and-proximity-based method is more accurate at determining agreement, and whether this translates to feelings of engagement from the players. Experimental results show that our procedural system was more engaging to players, and was more accurate at detecting agreement, reaching an average accuracy of 0.44 compared to 0.28 for the diarised system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
