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

PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training