Abstract
Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to the need to model complex interactions between entities across sentences. Current approaches, largely based on the ATLOP architecture, are commonly evaluated on benchmarks like DocRED and Re-DocRED. However, their performance in zero-shot or few-shot settings remains largely underexplored due to the task's complexity. Recently, the GLiNER model has shown that a compact NER model can outperform much larger Large Language Models. With a similar motivation, we introduce GLiDRE, a new model for document-level relation extraction that builds on the key ideas of GliNER. We benchmark GLiDRE against state-of-the-art models across various data settings on the Re-DocRED dataset. Our results demonstrate that GLiDRE achieves state-of-the-art performance in few-shot scenarios. Our code is publicly available.
Abstract (translated)
关系抽取（RE）是自然语言处理中的一个基本任务，其文档级别的变体由于需要建模实体在句子间的复杂交互而面临重大挑战。目前基于ATLOP架构的方法通常在DocRED和Re-DocRED等基准测试上进行评估。然而，由于该任务的复杂性，在零样本或少样本设置下的性能研究仍然不足。 最近，GLiNER模型展示了紧凑型命名实体识别（NER）模型可以在零样本和少样本场景下超越更大的语言模型。受到类似动机的启发，我们引入了GLiDRE，这是一种新的用于文档级关系抽取的模型，它基于GliNER的关键理念构建。我们在Re-DocRED数据集的各种数据设置上对GLiDRE进行了基准测试，并将其与其他最先进的模型进行比较。 我们的实验结果表明，在少样本场景下，GLiDRE达到了最佳性能水平。我们开源了代码以供社区使用。
URL
https://arxiv.org/abs/2508.00757
