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Unified Structure Generation for Universal Information Extraction

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arxiv 2203.12277 v1 pith:DXDXVOX6 submitted 2022-03-23 cs.CL

Yaojie Lu , Qing Liu , Dai Dai , Xinyan Xiao , Hongyu Lin , Xianpei Han , Le Sun , Hua Wu This is my paper · ORCID
classification cs.CL
keywords extractiondifferentstructurestasksabilitiesadaptivelygenerationinformation
verification ladder T0 review T1 audit T2 compute T3 formal
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Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.

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