Graph-Text Adversarial Distillation Model for Document-Level Joint Relation Extraction

Document-level relation extraction aims to identify relationships between entities across a document and plays a vital role in downstream tasks such as knowledge graph construction. However, most existing methods assume that entities and their coreferences are pre-annotated. This assumption can lead to error propagation and limit error correction, thereby degrading real-world performance. While recent approaches that integrate graph neural networks with Transformer-based models have shown promising results, the interaction between document encoders and graph encoders is often unidirectional and insufficient. Moreover, graph encoders frequently struggle to capture the global context of entity nodes, hindering their ability to model cross-sentence reasoning. To tackle these challenges, we propose a Graph-Transformer-based joint relation extraction model that employs a multi-graph joint modeling framework, including an entity aggregation graph, a semantic relation graph, and a global structural graph, to comprehensively represent semantic and structural associations among entities. We enhance a multi-layer relational graph convolutional network (R-GCN) with structure-aware self-attention and graph-text cross-attention. These mechanisms help better fuse structural and semantic information. Furthermore, we introduce a Graph-to-Text Adversarial Distillation (GTAD) mechanism to effectively transfer structural knowledge from the graph encoder to the document encoder. The experimental results demonstrate that the proposed method achieves superior performance across multiple document-level relation extraction tasks.