Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
| Autor: | Hanlei Zhang, Ting-En Lin, Hua Xu |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Feature engineering Computer Science - Machine Learning Computer Science - Computation and Language Forcing (recursion theory) business.industry Computer science General Medicine Overfitting Machine learning computer.software_genre Machine Learning (cs.LG) Task (project management) ComputingMethodologies_PATTERNRECOGNITION Cluster (physics) Benchmark (computing) Artificial intelligence business Cluster analysis Computation and Language (cs.CL) computer |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v34i05.6353 |
| Popis: | 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. Accepted by AAAI2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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