DSS-DocRE: A DenseNet-Based Semantic Segmentation Model for Document-Level Relation Extraction

Document-level relation extraction aims to discover inter-sentence relationships within articles through sophisticated reasoning. This task poses three key challenges: (i) recognizing complex entity combinations (one-to-many, many-to-one, many-to-many); (ii) handling entities appearing multiple times across documents, necessitating context-aware embeddings; (iii) focusing on evidence sentences for intricate relational reasoning. In response, we propose DSS-DocRE, a model based on DenseNet. To address the issue of repeated entities, we employ local context pooling, enhancing entity embeddings with additional context relevant to the entity pair. Furthermore, we construct an evidence classifier sub-task model to utilize evidence sentences for relational reasoning, integrating it into a multi-task learning framework for joint training. We also employ a dynamic class loss, replacing the global threshold with a learnable threshold class set, improving the loss function to balance the weights of common and uncommon classes. Finally, noting the current scarcity of document-level relation extraction corpora, a distilled model is used to reason over distant supervision data, enhancing model generalization. Through detailed experiments, we demonstrate that the DSS-DocRE model achieves better performance on the public datasets DocRED and ReDocRED. Additionally, experimental analysis verifies the relation segmentation network's efficacy in document-level relation extraction and its superior interpretability.