Unsupervised Fine-tuning for Text Clustering
Shaohan Huang, Furu Wei, Lei Cui, Xingxing Zhang, Ming Zhou
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.
BibTeX
@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://aclanthology.org/2020.coling-main.482/",
doi = "10.18653/v1/2020.coling-main.482",
pages = "5530--5534"
}