Title: CLUSTERBERT: MULTI-STAGE FINE-TUNING OF TRANSFORMERS FOR DEEP TEXT CLUSTERING. Abstract: Transformer models have originally been designed for text generation, classification and sequence labelling, and they have achieved new state-of-the-art results in those areas. Recent deep clustering methods learn cluster-friendly spaces for complex data and thereby outperform traditional clustering algorithms, especially on images and graphs. We propose ClusterBERT, an unsupervised algorithm that combines the strengths of both approaches. By tightly integrating transformer-based sentence representation learning with clustering, our method discovers a cluster-friendly representation of text data that retains useful semantic information. ClusterBERT is a multi-stage procedure that consists of domain adaptation, clustering, and hardening of the clusters. Starting from an initial representation obtained by transformer models, ClusterBERT learns a cluster-friendly space for text data by jointly optimizing the reconstruction loss and a clustering loss. Our experiments demonstrate that ClusterBERT outperforms state-of-the-art text clustering methods. 