New Intent Discovery with Pre-training and Contrastive Learning
Yuwei Zhang, Haode Zhang, Li-Ming Zhan, Xiao-Ming Wu, Albert Y. S. Lam · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes.It is a critical task for the development and service expansion of a practical dialogue system.Despite its importance, this problem remains under-explored in the literature.Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.In this paper, we provide new solutions to two important research questions for new intent discovery: (1) how to learn semantic utterance representations and (2) how to better cluster utterances.Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning.Then, we design a new contrastive loss to exploit self-supervisory signals in unlabeled data for clustering.Extensive experiments on three intent recognition benchmarks demonstrate the high effectiveness of our proposed method, which outperforms state-of-the-art methods by a large margin in both unsupervised and semi-supervised scenarios.The source code will be available at https://github. com/