Abstract
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.
Abstract (translated)
文档级关系抽取（DocRE）面临重大挑战，主要是由于其依赖跨句子的上下文以及关系类型的长尾分布，其中许多关系只有很少的训练样本。在本项工作中，我们引入了DOcument-level Relation Extraction optiMizing the long taIl (DOREMI)，这是一个迭代框架，通过最少但有针对性的手动注释来增强代表性不足的关系。与依赖大规模嘈杂数据或启发式去噪方法的先前方法不同，DOREMI主动选择最有信息量的例子以提高训练效率和鲁棒性。DOREMI可以应用于任何现有的DocRE模型，并且在减轻长尾偏差方面非常有效，提供了一种可扩展的方法来改善罕见关系上的泛化能力。
URL
https://arxiv.org/abs/2601.11190
