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

• URL: http://arxiv.org/abs/2205.10511v1
• Date: Sat, 21 May 2022 06:15:11 GMT
• Title: Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
• Authors: Yangkai Du, Tengfei Ma, Lingfei Wu, Yiming Wu, Xuhong Zhang, Bo Long, Shouling Ji
• Abstract summary: 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.
• Score: 66.83982926437547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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