  Universal Information Extraction (UIE) has been introduced as a unified
framework for various Information Extraction (IE) tasks and has achieved
widespread success. Despite this, UIE models have limitations. For example,
they rely heavily on span boundaries in the data during training, which does
not reflect the reality of span annotation challenges. Slight adjustments to
positions can also meet requirements. Additionally, UIE models lack attention
to the limited span length feature in IE. To address these deficiencies, we
propose the Fuzzy Span Universal Information Extraction (FSUIE) framework.
Specifically, our contribution consists of two concepts: fuzzy span loss and
fuzzy span attention. Our experimental results on a series of main IE tasks
show significant improvement compared to the baseline, especially in terms of
fast convergence and strong performance with small amounts of data and training
epochs. These results demonstrate the effectiveness and generalization of FSUIE
in different tasks, settings, and scenarios.
