  Existing text classification methods mainly focus on a fixed label set,
whereas many real-world applications require extending to new fine-grained
classes as the number of samples per label increases. To accommodate such
requirements, we introduce a new problem called coarse-to-fine grained
classification, which aims to perform fine-grained classification on coarsely
annotated data. Instead of asking for new fine-grained human annotations, we
opt to leverage label surface names as the only human guidance and weave in
rich pre-trained generative language models into the iterative weak supervision
strategy. Specifically, we first propose a label-conditioned finetuning
formulation to attune these generators for our task. Furthermore, we devise a
regularization objective based on the coarse-fine label constraints derived
from our problem setting, giving us even further improvements over the prior
formulation. Our framework uses the fine-tuned generative models to sample
pseudo-training data for training the classifier, and bootstraps on real
unlabeled data for model refinement. Extensive experiments and case studies on
two real-world datasets demonstrate superior performance over SOTA zero-shot
classification baselines.
