  Towards real-world information extraction scenario, research of relation
extraction is advancing to document-level relation extraction(DocRE). Existing
approaches for DocRE aim to extract relation by encoding various information
sources in the long context by novel model architectures. However, the inherent
long-tailed distribution problem of DocRE is overlooked by prior work. We argue
that mitigating the long-tailed distribution problem is crucial for DocRE in
the real-world scenario. Motivated by the long-tailed distribution problem, we
propose an Easy Relation Augmentation(ERA) method for improving DocRE by
enhancing the performance of tailed relations. In addition, we further propose
a novel contrastive learning framework based on our ERA, i.e., ERACL, which can
further improve the model performance on tailed relations and achieve
competitive overall DocRE performance compared to the state-of-arts.
