# LIME: Weakly-Supervised Text Classification Without Seeds

arXiv: https://arxiv.org/abs/2210.06720 · HTML: https://mlsanity.com/paper/2210.06720/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [X-Class: Text Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2010.12794/) (2020)
- [Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start](https://mlsanity.com/paper/2010.02584/) (2020)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches](https://mlsanity.com/paper/2305.12749/) (2023)
- [PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training](https://mlsanity.com/paper/2305.13723/) (2023)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [Less than One-shot: Named Entity Recognition via Extremely Weak Supervision](https://mlsanity.com/paper/2311.02861/) (2023)
- [Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification](https://mlsanity.com/paper/2305.14794/) (2023)
- [RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules](https://mlsanity.com/paper/2403.02932/) (2024)

## Abstract (arXiv)

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.