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Towards real-world information extraction scenario, research of relationextraction is advancing to document-level relation extraction(DocRE). Existingapproaches for DocRE aim to extract relation by encoding various informationsources in the long context by novel model architectures. However, the inherentlong-tailed distribution problem of DocRE is overlooked by prior work. We arguethat mitigating the long-tailed distribution problem is crucial for DocRE inthe real-world scenario. Motivated by the long-tailed distribution problem, wepropose an Easy Relation Augmentation(ERA) method for improving DocRE byenhancing the performance of tailed relations. In addition, we further proposea novel contrastive learning framework based on our ERA, i.e., ERACL, which canfurther improve the model performance on tailed relations and achievecompetitive overall DocRE performance compared to the state-of-arts.

Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning