Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
- Chen Shi ,
- Qi Chen ,
- Lei Shan ,
- Sujian Li ,
- Xu Sun ,
- Houfeng Wang ,
- Lintao Zhang
2018 Conference on Empirical Methods in Natural Language Processing |
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. Experimental results show that our framework can promote human labeling cost to a great extent, achieve good intent clustering accuracy (84.1%), and provide reasonable and instructive slot labeling results.