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        <title>FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction
%A Peng, Tianshuo
%A Li, Zuchao
%A Zhang, Lefei
%A Du, Bo
%A Zhao, Hai
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F peng-etal-2023-fsuie
%X 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.
%R 10.18653/v1/2023.acl-long.902
%U https://aclanthology.org/2023.acl-long.902
%U https://doi.org/10.18653/v1/2023.acl-long.902
%P 16318-16333

Markdown (Informal)

[FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction](https://aclanthology.org/2023.acl-long.902) (Peng et al., ACL 2023)

• FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction (Peng et al., ACL 2023)

ACL

• Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, and Hai Zhao. 2023. FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16318–16333, Toronto, Canada. Association for Computational Linguistics.