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


  • Xiaokang Liu China Automotive Technology and Research Center Co., Ltd.
  • Jianquan Li Beijing Ultrapower Software Co.,Ltd.
  • Jingjing Mu Beijing Ultrapower Software Co.,Ltd.,
  • Min Yang Chinese Academy of Sciences
  • Ruifeng Xu Harbin Institute of Technology (Shenzhen)
  • Benyou Wang The Chinese University of Hong Kong, Shenzhen




SNLP: Text Classification


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




How to Cite

Liu, X., Li, J., Mu, J., Yang, M., Xu, R., & Wang, B. (2023). Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 13291-13299. https://doi.org/10.1609/aaai.v37i11.26560



AAAI Technical Track on Speech & Natural Language Processing