  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.
