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Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
[article]
2019
arXiv
pre-print
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
arXiv:1911.08891v1
fatcat:uvaea6pvofd7rdjlfjxojf6ylq