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