An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
This addresses the problem of extracting structured information from unstructured text for natural language processing applications, though it appears incremental as it builds on existing generative approaches.
The paper tackles joint entity and relation extraction from text by proposing a span-based autoregressive framework that generates linearized graphs, achieving competitive results on benchmark datasets.
In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem. In contrast to conventional generative information extraction models that are left-to-right token-level generators, our approach is \textit{span-based}. It generates a linearized graph where nodes represent text spans and edges represent relation triplets. Our method employs a transformer encoder-decoder architecture with pointing mechanism on a dynamic vocabulary of spans and relation types. Our model can capture the structural characteristics and boundaries of entities and relations through span representations while simultaneously grounding the generated output in the original text thanks to the pointing mechanism. Evaluation on benchmark datasets validates the effectiveness of our approach, demonstrating competitive results. Code is available at https://github.com/urchade/ATG.