通过说话人切换模式提升多方对话意图识别效果
Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations
- 用说话人切换序列构建自监督目标，量化互动可预测性
- 在多个预训练模型上提升意图识别准确率
- 适合研究多人交互对话与自监督学习的学者
我们提出一种多任务学习方法，用于多方对话中的意图识别，该方法引入了一个辅助任务来建模说话人切换动态。具体而言，我们提出了说话人转换熵（turn-transition entropy），这是一个从说话人切换序列中计算出的自监督目标，用于量化互动模式的可预测性。在多个预训练模型上的实验表明，引入该辅助任务能显著提升意图识别性能，优于忽略多方交互动态的现有方法。我们发现，所提出的连续目标可作为单一任务目标进行学习，表明其确实携带了有用信息。
原文摘要 · Abstract (English)
We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynamics. Specifically, we introduce turn-transition entropy, a self-supervised target computed from the sequence of speaker transitions, which quantifies the predictability of interaction patterns. Experiments on multiple pre-trained models demonstrate that incorporating this auxiliary task improves intent recognition performance, outperforming existing approaches which ignore multi-party interaction dynamics. We find that our proposed continuous target can be learned as a single-task objective, suggesting that it is an actual signal carrying useful information.
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