LIME : Weakly-Supervised Text Classification without Seeds (2022)

Abstract: 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.

## introduction weakly-supervised text classification @xcite is an important avenue of research in lowresourced text classification. unlike in traditional text classification, all supervision derives from textual information in category names. weaklysupervised classification offers a practical approach to classification because it does not necessitate massive amounts of training data. another distinct aspect of weakly-supervised text classification is that the system has access to the entire test set at evaluation time, instead of encountering test samples sequentially. exploiting this characteristic, recent approaches employ keyword-matching pseudo-labeling schemes to tentatively assign class labels to each test sample, before using the information to train a separate classifier @xcite @xcite 1 labels identified with maximal entailment @xcite . pseudo-labels are assigned by counting how many "seed words" of each class are found in the test sample. keyword matching-based labeling, however, is neither adaptable nor flexible because semantic information embedded in class names cannot be extracted adaptively for distinct classification tasks. inspired by recent advances in prompt-based text classification @xcite @xcite , we replace the keywordbased pseudo-labeling step with a more streamlined entailment-based approach. extensive experiments show that entailment-based classifiers assign more accurate pseudo-labels with greater task adaptability and much fewer hyperparameters. we find that our method realizes the benefits of both entailmentbased classification and self-training. our contributions are as follows: ## the lime framework lime enhances the two-phase weakly-supervised classification pipeline with an entailment-based pseudo-labeling scheme.