论文标题
通过实体知识注入增强文档级别的关系提取
Enhancing Document-level Relation Extraction by Entity Knowledge Injection
论文作者
论文摘要
文档级关系提取（RE）旨在确定整个文档中实体之间的关系。它需要复杂的推理技能来综合各种知识，例如核心和常识。大规模知识图（kgs）包含大量现实世界事实，可以为文档级别提供宝贵的知识。在本文中，我们提出了一个实体知识注入框架，以增强当前的文档级RE模型。具体来说，我们将核心蒸馏介绍给注入核心知识，并具有更一般的核心推理能力。我们还采用代表对帐来注入事实知识，并将kg表示形式汇总到统一空间中。两个基准数据集的实验验证了我们实体知识注入框架的概括，并对多个文档级RE模型的一致改进。
Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.
