  Contrastive learning is a promising approach to unsupervised learning, as it
inherits the advantages of well-studied deep models without a dedicated and
complex model design. In this paper, based on bidirectional encoder
representations from transformers, we propose self-supervised contrastive
learning (SCL) as well as few-shot contrastive learning (FCL) with unsupervised
data augmentation (UDA) for text clustering. SCL outperforms state-of-the-art
unsupervised clustering approaches for short texts and those for long texts in
terms of several clustering evaluation measures. FCL achieves performance close
to supervised learning, and FCL with UDA further improves the performance for
short texts.
