Chen Shi
2018
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
Chen Shi
|
Qi Chen
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Lei Sha
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Sujian Li
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Xu Sun
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Houfeng Wang
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Lintao Zhang
Proceedings of the 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.
2016
Knowledge-Based Semantic Embedding for Machine Translation
Chen Shi
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Shujie Liu
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Shuo Ren
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Shi Feng
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Mu Li
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Ming Zhou
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Xu Sun
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Houfeng Wang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)