Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle to learn friendly representations to detect the open intent with prior knowledge of only known intents. Secondly, there is a lack of an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments demonstrate the effectiveness of the proposed distance-aware and boundary learning strategies. Compared to state-of-the-art methods, our framework achieves substantial improvements on three benchmark datasets. Furthermore, it yields robust performance with varying proportions of labeled data and known categories.

翻译：开放意图检测是自然语言理解中的一个重要问题，旨在识别未见过的开放意图，同时确保已知意图的识别性能。然而，现有方法面临两大挑战：首先，它们难以在仅已知意图先验知识的情况下学习友好表示以检测开放意图；其次，缺乏有效方法获取已知意图的特定且紧凑的决策边界。为解决这些问题，本文提出了一种原创框架DA-ADB，该框架逐步学习距离感知的意图表示和自适应决策边界以用于开放意图检测。具体而言，我们首先利用距离信息增强意图表示的区分能力，然后设计了一种新颖的损失函数，通过平衡经验风险和开放空间风险来获取合适的决策边界。大量实验证明了所提出的距离感知和边界学习策略的有效性。与现有最优方法相比，我们的框架在三个基准数据集上实现了显著提升。此外，该框架在不同比例的有标签数据和已知类别下均表现出稳健的性能。