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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
%A Shi, Chen
%A Chen, Qi
%A Sha, Lei
%A Li, Sujian
%A Sun, Xu
%A Wang, Houfeng
%A Zhang, Lintao
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shi-etal-2018-auto
%X 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.
%R 10.18653/v1/D18-1072
%U https://aclanthology.org/D18-1072
%U https://doi.org/10.18653/v1/D18-1072
%P 684-689

Markdown (Informal)

[Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning](https://aclanthology.org/D18-1072) (Shi et al., EMNLP 2018)

• Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning (Shi et al., EMNLP 2018)

ACL

• Chen Shi, Qi Chen, Lei Sha, Sujian Li, Xu Sun, Houfeng Wang, and Lintao Zhang. 2018. Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 684–689, Brussels, Belgium. Association for Computational Linguistics.