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.

翻译：领域外（OOD）意图检测对于实际对话系统至关重要，通常需要考虑多轮对话上下文。然而，以往大多数OOD意图检测方法局限于单轮对话。本文提出了一种上下文感知的OOD意图检测（Caro）框架，用于对OOD意图检测任务中的多轮上下文进行建模。具体而言，我们遵循信息瓶颈原理，从多轮对话上下文中提取鲁棒表征。为每个输入样本构建两种不同的视图，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。此外，我们还探索了在Caro中利用无标注数据的方法。引入两阶段训练过程从这些无标注数据中挖掘OOD样本，并通过自举方法使用这些OOD样本训练最终模型。综合实验表明，与先前最优方法相比，Caro将F1-OOD分数提升了超过29%，在多轮OOD检测任务上取得了最先进性能。