  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.
