LIME: Weakly-Supervised Text Classification without Seeds

Seongmin Park, Jihwa Lee


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.
Anthology ID:
2022.coling-1.91
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1083–1088
Language:
URL:
https://aclanthology.org/2022.coling-1.91
DOI:
Bibkey:
Cite (ACL):
Seongmin Park and Jihwa Lee. 2022. LIME: Weakly-Supervised Text Classification without Seeds. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1083–1088, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
LIME: Weakly-Supervised Text Classification without Seeds (Park & Lee, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.91.pdf
Code
 seongminp/lime
Data
AG News