轻量级模型提升少样本文档关系抽取效果
GLiDRE: Generalist Lightweight model for Document-level Relation Extraction
- 设计紧凑模型，兼顾监督与少样本学习
- 在少样本场景下超越现有方法，性能领先
- 适合资源有限但需高效关系抽取的场景
关系抽取（RE）是自然语言处理中的基础任务，其文档级变体因跨句实体间复杂交互而面临挑战。尽管监督模型在充分数据下表现优异，但在数据有限时的表现仍缺乏深入研究。我们提出GLiDRE，一种新型轻量级文档级关系抽取模型，可在监督和少样本设置下高效运行。在低资源监督训练与少样本元学习基准上的实验表明，该方法在数据受限场景下优于现有技术，确立了少样本文档级关系抽取的新基准。代码将公开。
原文摘要 · Abstract (English)
Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to complex interactions between entities across sentences. While supervised models have achieved strong results in fully resourced settings, their behavior with limited training data remains insufficiently studied. We introduce GLiDRE, a new compact model for document-level relation extraction, designed to work efficiently in both supervised and few-shot settings. Experiments in both low-resource supervised training and few-shot meta-learning benchmarks show that our approach outperforms existing methods in data-constrained scenarios, establishing a new state-of-the-art in few-shot document-level relation extraction. Our code will be publicly available.
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