  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
