Document-Level Relation Extraction (DocRE) presents significant challenges due to its reliance on cross-sentence context and the long-tail distribution of relation types, where many relations have scarce training examples. In this work, we introduce DOcument-level Relation Extraction optiMizing the long taIl (DOREMI), an iterative framework that enhances underrepresented relations through minimal yet targeted manual annotations. Unlike previous approaches that rely on large-scale noisy data or heuristic denoising, DOREMI actively selects the most informative examples to improve training efficiency and robustness. DOREMI can be applied to any existing DocRE model and is effective at mitigating long-tail biases, offering a scalable solution to improve generalization on rare relations.

翻译：文档级关系抽取（DocRE）因其对跨句子上下文的依赖以及关系类型的长尾分布而面临重大挑战，其中许多关系仅有稀少的训练样本。本研究提出了优化长尾分布的文档级关系抽取框架（DOREMI），该迭代框架通过最小化但有针对性的手动标注来增强低代表性关系。与以往依赖大规模噪声数据或启发式去噪的方法不同，DOREMI主动选择信息量最大的样本来提升训练效率和鲁棒性。DOREMI可应用于任何现有的DocRE模型，并能有效缓解长尾偏差，为提升稀有关系的泛化能力提供可扩展的解决方案。