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arXiv · 2611.09342

Syn2DRE: Synergistic Dual Teacher-Student Framework for Document-level Relation Extraction

Khai Phan Tran·Xue Li
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Abstract

Document-level Relation Extraction (DocRE) aims to identify relations between entities across a document but typically relies on costly large-scale annotations. While pseudo-labelling enables the use of low-quality data, existing methods often suffer from inefficiency and confirmation bias. To address these challenges, we propose Syn2DRE, a semi-supervised framework that combines self-supervised learning with cross-supervision in a lightweight teacher-student architecture to produce more reliable pseudo-labels. Additionally, we introduce a unified end-to-end training strategy to further improve efficiency of the framework. Experiments on multiple benchmarks show that Syn2DRE consistently outperforms prior state-of-the-art semi-supervised DocRE methods.

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Khai Phan Tran, Xue Li. 2026-11-06. Syn2DRE: Synergistic Dual Teacher-Student Framework for Document-level Relation Extraction. https://arxiv.org/abs/2611.09342

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