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

Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary