SimCTC: A Simple Contrast Learning Method of Text Clustering (Student Abstract)

This paper presents SimCTC, a simple contrastive learning (CL) method that greatly advances the state-of-the-art text clustering models. In SimCTC, a pre-trained BERT model first maps the input sequence to the representation space, which is then followed by three different loss function heads: Clustering head, Instance-CL head and Cluster-CL head. Experimental results on multiple benchmark datasets demonstrate that SimCTC remarkably outperforms 6 competitive text clustering methods with 1%-6% improvement on Accuracy (ACC) and 1%-4% improvement on Normalized Mutual Information (NMI). Moreover, our results also show that the clustering performance can be further improved by setting an appropriate number of clusters in the cluster-level objective.

Authors

• Chen Li College of Computer Science and Technology, Zhejiang Normal University, Zhejiang, 311231, China Research Institute of Hangzhou Artificial Intelligence, Zhejiang Normal University, Hangzhou, Zhejiang, 311231, China
• Xiaoguang Yu
• Shuangyong Song China Telecom Corporation Ltd. Data&AI Technology Company
• Jia Wang
• Bo Zou JD AI Research, Beijing
• Xiaodong He JD AI, Beijing

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