@inproceedings{huang-etal-2020-unsupervised,
    title = "Unsupervised Fine-tuning for Text Clustering",
    author = "Huang, Shaohan  and
      Wei, Furu  and
      Cui, Lei  and
      Zhang, Xingxing  and
      Zhou, Ming",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.coling-main.482/",
    doi = "10.18653/v1/2020.coling-main.482",
    pages = "5530--5534",
    abstract = "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 state-of-the-art results."
}