Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification

Seongmin Park, Kyungho Kim, Jihwa Lee


Abstract
Text classification with extremely weak supervision (EWS) imposes stricter supervision constraints compared to regular weakly supervise classification. Absolutely no labeled training samples or hand-crafted rules specific to the evaluation data are allowed. Such restrictions limit state-of-the-art EWS classification methods to indirect weak labeling techniques that assign unnatural label uncertainty estimates. We present PLAT, a framework that creates weak labels by leveraging recent developments in zero-shot text classification. PLAT employs models trained for sub-tasks other than classification to label documents. Most importantly, PLAT refrains from assigning overly confident weak labels and improves soft-label training performance for downstream classifiers. Classifiers trained with PLAT significantly outperform those trained on weak labels generated by the previous state-of-the-art in extremely weakly supervised text classification.
Anthology ID:
2023.findings-acl.328
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5329–5341
Language:
URL:
https://aclanthology.org/2023.findings-acl.328
DOI:
10.18653/v1/2023.findings-acl.328
Bibkey:
Cite (ACL):
Seongmin Park, Kyungho Kim, and Jihwa Lee. 2023. Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5329–5341, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification (Park et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.328.pdf