Out-of-Domain (OOD) intent detection is vital for practical dialogue systems,
and it usually requires considering multi-turn dialogue contexts. However, most
previous OOD intent detection approaches are limited to single dialogue turns.
In this paper, we introduce a context-aware OOD intent detection (Caro)
framework to model multi-turn contexts in OOD intent detection tasks.
Specifically, we follow the information bottleneck principle to extract robust
representations from multi-turn dialogue contexts. Two different views are
constructed for each input sample and the superfluous information not related
to intent detection is removed using a multi-view information bottleneck loss.
Moreover, we also explore utilizing unlabeled data in Caro. A two-stage
training process is introduced to mine OOD samples from these unlabeled data,
and these OOD samples are used to train the resulting model with a
bootstrapping approach. Comprehensive experiments demonstrate that Caro
establishes state-of-the-art performances on multi-turn OOD detection tasks by
improving the F1-OOD score of over $29\%$ compared to the previous best method.

本文提出一种上下文感知的 out-of-domain 意图检测（Caro）框架来模拟多轮对话中的上下文，采用信息瓶颈原理从多轮对话上下文中提取鲁棒特征，并通过多视图信息瓶颈损失去除与意图检测无关的冗余信息，在 Caro 中探索了利用未标记的数据，这种方法提升了 29% 以上的 F1-ODD 分数，实验表明 Caro 在多轮意图检测任务中取得了最好的表现。