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

Contrastive Bootstrapping for Label Refinement