Unsupervised Fine-tuning for Text Clustering
Shaohan Huang, Furu Wei, Lei Cui, Xingxing Zhang, Ming Zhou · 2020
Fine-tuning with pre-trained language models (e.g.BERT) has achieved great success in many language understanding tasks in supervised settings (e.g.text classification).However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering.In this paper, we propose a novel method to fine-tune pre-trained models unsupervisedly for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss.Experiments on three text clustering datasets (namely TREC-6, Yelp, and DBpedia) show that our model outperforms the baseline methods and achieves stateof-the-art results.