E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Abstract
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.
Anthology ID:
2023.findings-acl.103
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1619–1634
Language:
URL:
https://aclanthology.org/2023.findings-acl.103
DOI:
10.18653/v1/2023.findings-acl.103
Bibkey:
Cite (ACL):
Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, and Bingzhe Wu. 2023. E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1619–1634, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (Zhang et al., Findings 2023)
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