ATGL: An Adaptive-Threshold Global Loss for Document-level Relation Extraction
TL;DR
The ATGL serves as a general optimization objective that can be readily applied to different DocRE models and outperforms other DocRE losses and achieves state-of-the-art results, while consistently improving the performance of existing DocRE models.
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 A daptive-T hreshold G lobal L oss (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. Code is available at https: //github.com/xhm-code/ATGL .