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arXiv · 2205.10511

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

Yangkai Du·Tengfei Ma·Lingfei Wu·Yiming Wu·Xuhong Zhang·Bo Long·Shouling Ji
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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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Yangkai Du, Tengfei Ma, Lingfei Wu, Yiming Wu, Xuhong Zhang, Bo Long, Shouling Ji. 2022-05-21. Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning. https://arxiv.org/abs/2205.10511

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