发表机构
University of Padua（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对DocRE依赖人工标注、远程监督资源利用不足的问题，提出本体驱动框架强化结构一致性，减少逻辑矛盾，提升泛化性能，为利用大规模远程数据提供有效策略。
AI 中文摘要
文档级关系抽取（DocRE）高度依赖成本高昂的人工标注数据集，而DocRED distant等大规模远程监督资源因存在噪声而未得到充分利用。研究发现，噪声的一个关键且被忽视的来源在于关系三元组内部的结构不一致性，包括违反本体约束和逻辑矛盾。本文提出一种本体驱动的框架，用于量化和强化DocRE数据集的结构一致性。分析显示，DocRED distant中存在大量结构噪声，且此类不一致性会传播至模型预测结果；在训练过程中强化结构规范性可显著减少逻辑矛盾，并持续提升泛化性能。这些发现确立了结构一致性是DocRE中缺失的监督维度，凸显结构正则化是大规模利用远程数据的有效策略。
英文摘要
Document-Level Relation Extraction (DocRE) relies heavily on costly manually annotated datasets, while large distant supervision resources such as DocRED distant remain underexploited due to noise. We show that a critical yet overlooked source of noise lies in structural inconsistencies within relational triples, including violations of ontology constraints and logical contradictions. We introduce an ontology-driven framework to quantify and enforce structural consistency in DocRE datasets. Our analysis reveals substantial structural noise in DocRED distant and demonstrates that such inconsistencies propagate to model predictions. Enforcing structural well-formedness during training significantly reduces logical contradictions and consistently improves generalization performance. These findings establish structural consistency as a missing axis of supervision in DocRE and highlight structural regularization as an effective strategy for leveraging distant data at scale.
CommentsAccepted at EMNLP 2026
