LIME: Weakly-Supervised Text Classification Without Seeds

• URL: http://arxiv.org/abs/2210.06720v1
• Date: Thu, 13 Oct 2022 04:28:28 GMT
• Title: LIME: Weakly-Supervised Text Classification Without Seeds
• Authors: Seongmin Park, Jihwa Lee
• Abstract summary: In weakly-supervised text classification, only label names act as sources of supervision. We present LIME, a framework for weakly-supervised text classification. We find that combining weakly-supervised classification and textual entailment mitigates shortcomings of both.
• Score: 1.2691047660244335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. We open source our code at https://github.com/seongminp/LIME.

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