  We propose a new paradigm for universal information extraction (IE) that is
compatible with any schema format and applicable to a list of IE tasks, such as
named entity recognition, relation extraction, event extraction and sentiment
analysis. Our approach converts the text-based IE tasks as the token-pair
problem, which uniformly disassembles all extraction targets into joint span
detection, classification and association problems with a unified extractive
framework, namely UniEX. UniEX can synchronously encode schema-based prompt and
textual information, and collaboratively learn the generalized knowledge from
pre-defined information using the auto-encoder language models. We develop a
traffine attention mechanism to integrate heterogeneous factors including
tasks, labels and inside tokens, and obtain the extraction target via a scoring
matrix. Experiment results show that UniEX can outperform generative universal
IE models in terms of performance and inference-speed on $14$ benchmarks IE
datasets with the supervised setting. The state-of-the-art performance in
low-resource scenarios also verifies the transferability and effectiveness of
UniEX.
