Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
Shi Chen, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, Lintao Zhang · 2018
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.