New Intent Discovery with Multi-View Clustering

Han Liu, Junjie Sun, Xiaotong Zhang, Hongyang Chen

Abstract: New intent discovery aims to identify new intents from unlabeled utterances in the open-world scenario. As the fundamental and challenging problem in dialogue systems, new intent discovery attracts increasing attention but is still under exploration. In this paper, we propose a simple and effective new intent discovery framework with multi-view clustering. Specifically, we first adopt a double-branch representation learning strategy to learn high-quality utterance representations. Then we conduct a multi-view clustering method to obtain the satisfactory cluster assignment in an iterative manner, thus fulfilling the new intent discovery task. Extensive experimental results on three widely-used datasets demonstrate that our proposed method outperforms other strong baselines in most cases.