Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

20 Nov 2019  ·  Ting-En Lin, Hua Xu, Hanlei Zhang ·

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.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Open Intent Discovery ATIS CDAC+ NMI 94.74 # 1
ARI 89.41 # 1
ACC 91.66 # 1
Open Intent Discovery SNIPS CDAC+ NMI 89.3 # 2
ARI 86.82 # 2
ACC 93.63 # 2
Open Intent Discovery Stackoverflow CDAC+ NMI 69.84 # 2
ARI 52.59 # 2
ACC 73.48 # 2

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