论文标题
文档级别的关系提取和上下文指导的提及集成和对层间推理
Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning
论文作者
论文摘要
文档级别的关系提取（DRE）旨在认识两个实体之间的关系。该实体可能对应于范围超出句子边界的多个提及。以前的研究很少研究提及的整合，这可能是有问题的，因为核心提及并未同样促进特定关系。此外，先前的努力主要集中在实体级别的推理上，而不是捕获实体对之间的全球互动。在本文中，我们提出了两种新颖的技术，即环境指导提及的集成和面间推理（CGM2IR），以改善DRE。不用简单地应用平均池，而是利用上下文来指导以加权总和的方式集成核心提及。另外，对配对推理在实体对图上执行迭代算法，以建模关系的相互依存关系。我们在三个广泛使用的基准数据集上评估了我们的CGM2IR模型，即DOCRED，CDR和GDA。实验结果表明，我们的模型表现优于先前的最新模型。
Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation. Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between entity pairs. In this paper, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve the DRE. Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner. Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations. We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous state-of-the-art models.
