ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
Submission Type: Regular Short Paper
Submission Track: Machine Learning for NLP
Submission Track 2: Information Extraction
Keywords: weak supervision, cross-validation, denoising methods
TL;DR: We propose a method ULF that builds on cross-validation to adjust the assignment of labeling functions to classes for weakly supervised learning.
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.
Submission Number: 5184
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