Discovering New Intents Via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this chapter, we introduce constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, the clusters are refined by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, the approach presented is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that the method presented can yield significant improvements over strong baselines.