Few-shot Named Entity Recognition with Self-describing Networks

Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun


Abstract
Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources. In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set. Specifically, we design Self-describing Networks (SDNet), a Seq2Seq generation model which can universally describe mentions using concepts, automatically map novel entity types to concepts, and adaptively recognize entities on-demand. We pre-train SDNet with large-scale corpus, and conduct experiments on 8 benchmarks from different domains. Experiments show that SDNet achieves competitive performances on all benchmarks and achieves the new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and robustness.
Anthology ID:
2022.acl-long.392
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5711–5722
Language:
URL:
https://aclanthology.org/2022.acl-long.392
DOI:
10.18653/v1/2022.acl-long.392
Bibkey:
Cite (ACL):
Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, and Le Sun. 2022. Few-shot Named Entity Recognition with Self-describing Networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5711–5722, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Few-shot Named Entity Recognition with Self-describing Networks (Chen et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.392.pdf
Software:
 2022.acl-long.392.software.zip
Code
 chen700564/sdnet
Data
WNUT 2017