Universally modeling all typical information extraction tasks (UIE) with one
generative language model (GLM) has revealed great potential by the latest
study, where various IE predictions are unified into a linearized hierarchical
expression under a GLM. Syntactic structure information, a type of effective
feature which has been extensively utilized in IE community, should also be
beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully
unleashing the power of syntactic knowledge for UIE. A heterogeneous structure
inductor is explored to unsupervisedly induce rich heterogeneous structural
representations by post-training an existing GLM. In particular, a structural
broadcaster is devised to compact various latent trees into explicit high-order
forests, helping to guide a better generation during decoding. We finally
introduce a task-oriented structure fine-tuning mechanism, further adjusting
the learned structures to most coincide with the end-task's need. Over 12 IE
benchmarks across 7 tasks our system shows significant improvements over the
baseline UIE system. Further in-depth analyses show that our GLM learns rich
task-adaptive structural bias that greatly resolves the UIE crux, the
long-range dependence issue and boundary identifying. Source codes are open at
this https URL

本研究旨在通过利用句法结构信息，提出一种新颖的结构感知生成式语言模型，对各种信息提取任务进行统一的建模与预测，并且通过引入异构结构感知器和任务导向的结构微调机制来实现更好的指导解码，从而显著提高了 12 项信息提取任务的性能。