  Weakly-supervised text classification trains a classifier using the label
name of each target class as the only supervision, which largely reduces human
annotation efforts. Most existing methods first use the label names as static
keyword-based features to generate pseudo labels, which are then used for final
classifier training. While reasonable, such a commonly adopted framework
suffers from two limitations: (1) keywords can have different meanings in
different contexts and some text may not have any keyword, so keyword matching
can induce noisy and inadequate pseudo labels; (2) the errors made in the
pseudo label generation stage will directly propagate to the classifier
training stage without a chance of being corrected. In this paper, we propose a
new method, PIEClass, consisting of two modules: (1) a pseudo label acquisition
module that uses zero-shot prompting of pre-trained language models (PLM) to
get pseudo labels based on contextualized text understanding beyond static
keyword matching, and (2) a noise-robust iterative ensemble training module
that iteratively trains classifiers and updates pseudo labels by utilizing two
PLM fine-tuning methods that regularize each other. Extensive experiments show
that PIEClass achieves overall better performance than existing strong
baselines on seven benchmark datasets and even achieves similar performance to
fully-supervised classifiers on sentiment classification tasks.
