ML2FNet: A Simple but Effective Multi-level Feature Fusion Network for Document-Level Relation Extraction

Document-level relation extraction presents new challenges compared to its sentence-level counterpart, which aims to extract relations from multiple sentences. Current graph-based and transformer-based models have achieved certain success. However, most approaches focus only on local information, independently on a certain entity, without considering the global interdependency among the relational triples. To solve this problem, this paper proposes a novel relation extraction model with a Multi-Level Feature Fusion Network (ML2FNet). Specifically, we first establish the interaction between entities by constructing an entity-level relation matrix. Then, we employ an enhanced U-shaped network to fuse the multi-level feature of entity pairs from local to global. Finally, the relation classification of entity pairs is performed by a bilinear classifier. We conduct experiments on three public document-level relation extraction datasets, and the results show that ML2FNet outperforms the other baselines. Our code is available at https://github.com/zzhinlp/ML2FNet .