Year: 2026
Type: conference-paper
Abstract: Document-level relation extraction (DocRE)aims to determine which relations hold between a given entity pair within a document.As a multi-label classification task, the most commonly adopted paradigm introduces a learnable threshold to distinguish positive and negative classes for an entity pair.Under this paradigm, existing losses decouple the optimization into independent positive and negative losses, which interact solely with a shared threshold.This leads to two inherent limitations: (i) threshold instability caused by conflicting gradient updates from the decoupled losses; and (ii) optimization bias exacerbated by the severe imbalance between limited positive samples and abundant negative samples inherent in DocRE, which makes the model more likely to predict that no relation exists.To address these issues, we propose the Adaptive-Threshold Global Loss (ATGL).Unlike prior work, ATGL integrates positive, negative, and threshold optimization into a unified logit space and explicitly enforces ranking constraints on their contributions to the objective.Furthermore, ATGL incorporates an imbalance-aware optimization mechanism, thereby effectively addressing the severe class imbalance in DocRE.Our ATGL serves as a general optimization objective that can be readily applied to different DocRE models.Experiments on four datasets show that ATGL outperforms other DocRE losses and achieves state-of-the-art results, while consistently improving the performance of existing DocRE models.
Institution: Northeastern University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G2799773603
OpenAlex ID: W7166840517