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In weakly-supervised text classification, only label names act as sources ofsupervision. Predominant approaches to weakly-supervised text classificationutilize a two-phase framework, where test samples are first assignedpseudo-labels and are then used to train a neural text classifier. In mostprevious work, the pseudo-labeling step is dependent on obtaining seed wordsthat best capture the relevance of each class label. We present LIME, aframework for weakly-supervised text classification that entirely replaces thebrittle seed-word generation process with entailment-basedpseudo-classification. We find that combining weakly-supervised classificationand textual entailment mitigates shortcomings of both, resulting in a morestreamlined and effective classification pipeline. With just an off-the-shelftextual entailment model, LIME outperforms recent baselines inweakly-supervised text classification and achieves state-of-the-art in 4benchmarks. We open source our code at https://github.com/seongminp/LIME.

LIME: Weakly-Supervised Text Classification Without Seeds