Abstract
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes. In this work, we investigate noise reduction techniques for WS based on the principle of k-fold cross-validation. We introduce a new algorithm ULF for Unsupervised Labeling Function correction, which denoises WS data by leveraging models trained on all but some LFs to identify and correct biases specific to the held-out LFs. Specifically, ULF refines the allocation of LFs to classes by re-estimating this assignment on highly reliable cross-validated samples. Evaluation on multiple datasets confirms ULF’s effectiveness in enhancing WS learning without the need for manual labeling.- Anthology ID:
- 2023.emnlp-main.254
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4162–4176
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.254
- DOI:
- 10.18653/v1/2023.emnlp-main.254
- Cite (ACL):
- Anastasiia Sedova and Benjamin Roth. 2023. ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4162–4176, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision (Sedova & Roth, EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.254.pdf