Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Authors

  • Ting-En Lin Tsinghua University
  • Hua Xu Tsinghua University
  • Hanlei Zhang Tsinghua University

DOI:

https://doi.org/10.1609/aaai.v34i05.6353

Abstract

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 paper, we propose 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, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines. 1

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Published

2020-04-03

How to Cite

Lin, T.-E., Xu, H., & Zhang, H. (2020). Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8360-8367. https://doi.org/10.1609/aaai.v34i05.6353

Issue

Section

AAAI Technical Track: Natural Language Processing