RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
This work addresses the problem of extracting diverse and complex information schemas in NLP, offering a more authentic UIE solution that improves generalization and performance, particularly in low-resource scenarios.
The paper tackles the challenge of Universal Information Extraction (UIE) by proposing RexUIE, a recursive method with an explicit schema instructor, which achieves state-of-the-art results on extracting complex schemas like quadruples and quintuples under both full-shot and few-shot settings.
Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. However, previous works have only achieved limited success by unifying a few tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), which fall short of being authentic UIE models particularly when extracting other general schemas such as quadruples and quintuples. Additionally, these models used an implicit structural schema instructor, which could lead to incorrect links between types, hindering the model's generalization and performance in low-resource scenarios. In this paper, we redefine the authentic UIE with a formal formulation that encompasses almost all extraction schemas. To the best of our knowledge, we are the first to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE. To avoid interference between different types, we reset the position ids and attention mask matrices. RexUIE shows strong performance under both full-shot and few-shot settings and achieves State-of-the-Art results on the tasks of extracting complex schemas.