Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification. First, we pre-train a feature encoder on the labeled training instances, which transfers knowledge from known intents to unknown intents. Specifically, we devise a K-center contrastive learning algorithm to learn discriminative and balanced intent features, improving the generalization of the model for recognizing open intents. Second, we devise an adjustable decision boundary learning method with expanding and shrinking (ADBES) to determine the suitable decision conditions. Concretely, we learn a decision boundary for each known intent class, which consists of a decision center and the radius of the decision boundary. We then expand the radius of the decision boundary to accommodate more in-class instances if the out-of-class instances are far from the decision boundary; otherwise, we shrink the radius of the decision boundary. Extensive experiments on three benchmark datasets clearly demonstrate the effectiveness of our method for open intent classification. For reproducibility, we submit the code at: https://github.com/lxk00/CLAP

翻译：开放意图分类旨在将已知意图正确归类至对应类别，同时识别新出现的未知（开放）意图，这是对话系统中一项重要但具有挑战性的任务。本文提出新型K中心对比学习与可调决策边界学习（CLAB）方法，以提升开放意图分类的有效性。首先，我们在标注训练实例上预训练特征编码器，将知识从已知意图迁移至未知意图。具体而言，我们设计了K中心对比学习算法来学习具有判别性与均衡性的意图特征，从而提升模型识别开放意图的泛化能力。其次，我们设计了带有扩张与收缩机制的可调决策边界学习方法（ADBES）来确定合适的决策条件。具体来说，我们为每个已知意图类别学习一个由决策中心与决策边界半径组成的决策边界。当类外实例远离决策边界时，我们扩大决策边界半径以容纳更多类内实例；否则，收缩决策边界半径。在三个基准数据集上的大量实验清晰证明了本文方法在开放意图分类中的有效性。为促进研究可复现性，我们提交代码至：https://github.com/lxk00/CLAP