Constrained Self-Supervised Clustering for Discovering New Intents (Student Abstract)

Authors

  • Ting-En Lin Tsinghua University
  • Hua Xu Tsinghua University
  • Hanlei Zhang Beijing Jiaotong University

DOI:

https://doi.org/10.1609/aaai.v34i10.7204

Abstract

Discovering new user intents is an emerging task in the dialogue system. In this paper, we propose a self-supervised clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process and does not require intensive feature engineering. Extensive experiments on three benchmark datasets show that our method can yield significant improvements over strong baselines.

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Published

2020-04-03

How to Cite

Lin, T.-E., Xu, H., & Zhang, H. (2020). Constrained Self-Supervised Clustering for Discovering New Intents (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13863-13864. https://doi.org/10.1609/aaai.v34i10.7204

Issue

Section

Student Abstract Track