Constrained Self-Supervised Clustering for Discovering New Intents (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v34i10.7204Abstract
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.
Downloads
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