mlsanityData to 14 Oct 2026

FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction

arXiv 2306.14913 ↗First posted 19 Jun 2023cs.CL cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 29 of its 44 references that are in the map. First posted on arXiv 19 Jun 2023.

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29 of 44 references.

References · 29

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17 more
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Citing papers per year: 2023: 1; 2024: 4; 2025: 2.

7 papers.