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://preview.aclanthology.org/build-pipeline-with-new-library/2023.findings-acl.328/
- DOI:
- 10.18653/v1/2023.findings-acl.328
- 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)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.findings-acl.328.pdf