CommDRE:Document-level Relation Extraction with Self-supervised Commonsense Learning

Document-level relation extraction (DocRE) is a more challenging task for which multi-label and multi-entity problems need to be resolved effectively than its sentence-level counterpart. It aims at extracting relationships between two entities at once while taking into account significant cross-sentence features and long-distance semantic representation. In this paper, we propose a self-supervised commonsense-enhanced DocRE model, called CommDRE, without external knowledge. First, we introduce self-supervised learning to represent the commonsense knowledge of each entity in an entity pair. Second, we convert the cross-sentence entity pairs into anonymous entity pairs with a coreference commonsense alternative. Finally, we perform semantic relation representation learning on the anonymous entity pairs and automatically convert them into target entity pairs. Experimental results show that it performs significantly better than strong baselines by 2.76% F1, and commonsense knowledge has an important contribution to the DocRE through the ablation study.