  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.
