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    <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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Fine-tuning for Text Clustering
%A Huang, Shaohan
%A Wei, Furu
%A Cui, Lei
%A Zhang, Xingxing
%A Zhou, Ming
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F huang-etal-2020-unsupervised
%X 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.
%R 10.18653/v1/2020.coling-main.482
%U https://aclanthology.org/2020.coling-main.482
%U https://doi.org/10.18653/v1/2020.coling-main.482
%P 5530-5534

Markdown (Informal)

[Unsupervised Fine-tuning for Text Clustering](https://aclanthology.org/2020.coling-main.482) (Huang et al., COLING 2020)

• Unsupervised Fine-tuning for Text Clustering (Huang et al., COLING 2020)

ACL

• Shaohan Huang, Furu Wei, Lei Cui, Xingxing Zhang, and Ming Zhou. 2020. Unsupervised Fine-tuning for Text Clustering. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5530–5534, Barcelona, Spain (Online). International Committee on Computational Linguistics.