论文标题
一种令人沮丧的简单方法，用于实体和关系提取
A Frustratingly Easy Approach for Entity and Relation Extraction
论文作者
论文摘要
端到端关系提取旨在识别指定的实体并提取它们之间的关系。最近的工作将这两个子任务共同模拟这两个子任务，要么通过将它们施放在一个结构化的预测框架中，要么通过共享表示形式进行多任务学习。在这项工作中，我们提出了一种简单的实体和关系提取方法，并在标准基准（ACE04，ACE05和SCIERC）上建立了新的最先进的方法，与先前具有相同预训练的编码器的关节模型相比，相对于先前的关节模型，相对于先前的关节模型，相对于以前的f1获得了1.7％-2.8％。我们的方法基本上建立在两个独立编码器上，仅使用实体模型来构建关系模型的输入。通过一系列仔细的考试，我们验证了学习实体和关系的不同上下文表示的重要性，在关系模型的早期将实体信息融合并融合了全球环境。最后，我们还对我们的方法提出了一个有效的近似值，该方法仅需要一个实体和关系编码的一个通过，在推理时间进行编码，实现了8-16美元的$ \ times $速度，而精度的略有降低。
End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. Our approach essentially builds on two independent encoders and merely uses the entity model to construct the input for the relation model. Through a series of careful examinations, we validate the importance of learning distinct contextual representations for entities and relations, fusing entity information early in the relation model, and incorporating global context. Finally, we also present an efficient approximation to our approach which requires only one pass of both entity and relation encoders at inference time, achieving an 8-16$\times$ speedup with a slight reduction in accuracy.
