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arXiv · 2306.04544

Contrastive Bootstrapping for Label Refinement

Shudi Hou·Yu Xia·Muhao Chen·Sujian Li
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Abstract

Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services. In this work, we investigate the setting where fine-grained classification is done only using the annotation of coarse-grained categories and the coarse-to-fine mapping. We propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. During clustering, it pulls away negative passage-prototype pairs under the guidance of the mapping from both global and local perspectives. Experiments on NYT and 20News show that our method outperforms the state-of-the-art methods by a large margin.

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Shudi Hou, Yu Xia, Muhao Chen, Sujian Li. 2023-06-07. Contrastive Bootstrapping for Label Refinement. https://arxiv.org/abs/2306.04544

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