PCBERT: Parent and Child BERT for Chinese Few-shot NER

Peichao Lai, Feiyang Ye, Lin Zhang, Zhiwei Chen, Yanggeng Fu, Yingjie Wu, Yilei Wang


Abstract
Achieving good performance on few-shot or zero-shot datasets has been a long-term challenge for NER. The conventional semantic transfer approaches on NER will decrease model performance when the semantic distribution is quite different, especially in Chinese few-shot NER. Recently, prompt-tuning has been thoroughly considered for low-resource tasks. But there is no effective prompt-tuning approach for Chinese few-shot NER. In this work, we propose a prompt-based Parent and Child BERT (PCBERT) for Chinese few-shot NER. To train an annotating model on high-resource datasets and then discover more implicit labels on low-resource datasets. We further design a label extension strategy to achieve label transferring from high-resource datasets. We evaluated our model on Weibo and the other three sampling Chinese NER datasets, and the experimental result demonstrates our approach’s effectiveness in few-shot learning.
Anthology ID:
2022.coling-1.192
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2199–2209
Language:
URL:
https://aclanthology.org/2022.coling-1.192
DOI:
Bibkey:
Cite (ACL):
Peichao Lai, Feiyang Ye, Lin Zhang, Zhiwei Chen, Yanggeng Fu, Yingjie Wu, and Yilei Wang. 2022. PCBERT: Parent and Child BERT for Chinese Few-shot NER. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2199–2209, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
PCBERT: Parent and Child BERT for Chinese Few-shot NER (Lai et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.192.pdf