Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

摘要

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.

出版物
Association for the Advancement of Artificial Intelligence (CCF A类会议)
林廷恩
硕士

2017年进组,2020年获得硕士学位,清华大学计算机系优秀硕士毕业生。

徐华
徐华
长聘副教授, Expert Systems with Application 副主编,博士生导师
张瀚镭
张瀚镭
博士研究生

我的研究方向为多模态对话意图发现。