Go to ICLR 2026 Conference homepageMD-RE: A Multi-Discrimination Framework for Document-Level Relation Extraction with Adaptive Threshold Shifted Loss
Keywords: Relation Extraction; Document-Level Relation Extraction; Adaptive Threshold Loss
TL;DR: A novel framework and loss function are proposed for document-level relation extraction.
Abstract: Document-level relation extraction (DocRE) aims to identify relations for an entity pair within a document. Existing methods can be broadly classified into two categories: direct encoding of the entire document or enhancement using extracted evidence sentences. However, the former often introduces noise unrelated to relations, while the latter is heavily dependent on the quality of evidence extraction. Moreover, these DocRE models typically use an adaptive threshold to predict all potential relations for an entity pair. As a result, class imbalance in DocRE often leads the model to learn a high throshold for an entity pair, which in turn causes the model to frequently predict that the entity pair has no relation. To address these issues, we propose a **M**ulti-**D**iscrimination framework (**MD-RE**) that does not rely on evidence sentences. MD-RE employs three discriminators with dynamically adjusted thresholds to independently predict relations, and aggregates their outputs via a weighted fusion strategy. Furthermore, we propose an **A**daptive **T**hreshold **S**hifted **L**oss (**ATSL**), which encourages lower threshold to alleviate the high false negative rate resulting from class imbalance. Experiments on three datasets demonstrate that our MD-RE framework with ATSL achieves new state-of-the-art results. Moreover, ATSL significantly improves the performance of various existing DocRE models. In addition, combining other losses with MD-RE also yields competitive results. Our code is available at https://anonymous.4open.science/r/MD-RE.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 10951
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