Text-to-Graph extraction aims to automatically extract information graphs
consisting of mentions and types from natural language texts. Existing
approaches, such as table filling and pairwise scoring, have shown impressive
performance on various information extraction tasks, but they are difficult to
scale to datasets with longer input texts because of their second-order
space/time complexities with respect to the input length. In this work, we
propose a Hybrid Span Generator (HySPA) that invertibly maps the information
graph to an alternating sequence of nodes and edge types, and directly
generates such sequences via a hybrid span decoder which can decode both the
spans and the types recurrently in linear time and space complexities.
Extensive experiments on the ACE05 dataset show that our approach also
significantly outperforms state-of-the-art on the joint entity and relation
extraction task.

本文介绍了一种名为 Hybrid Span Generator (HySPA) 的混合跨度生成器，它可以对文本中的信息图进行可逆映射，直接生成节点和边类型的交替序列，并在线性时间和空间复杂度内对跨度和类型进行解码。在 ACE05 数据集上的实验表明，该方法在联合实体和关系提取任务上也显着优于现有技术。