@inproceedings{sedova-roth-2023-ulf,
    title = "{ULF}: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision",
    author = "Sedova, Anastasiia  and
      Roth, Benjamin",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.emnlp-main.254/",
    doi = "10.18653/v1/2023.emnlp-main.254",
    pages = "4162--4176",
    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."
}