LCDS: Logic-Constrained Distant Supervision for Document-Level Relation Extraction

Abstract

Document-level relation extraction (DocRE) identifies semantic relations between entity pairs in a document, but its progress is limited by the scarcity of high-quality annotations. Distant supervision alleviates this issue by automatically constructing large-scale training data, yet it also introduces noisy and incomplete labels. To address these problems, we propose LCDS, a logic-constrained distant supervision framework for DocRE. LCDS first applies Monte Carlo dropout to estimate prediction uncertainty and filter unreliable pseudo labels. It then mines high-confidence logical rules from human-annotated data and uses them to complement missing relations and correct conflicts. Through iterative training, LCDS progressively improves pseudo-label quality. Experiments on DocRED and Re-DocRED show that LCDS achieves F1 scores of 67.39% and $\mathbf{8 1. 4 2 \%}$, respectively, outperforming previous state-of-theart models and demonstrating the effectiveness of combining uncertainty estimation with logical reasoning.