Abstract
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.- Anthology ID:
- 2021.findings-emnlp.139
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1618–1630
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.139
- DOI:
- 10.18653/v1/2021.findings-emnlp.139
- Cite (ACL):
- Yaqing Wang, Haoda Chu, Chao Zhang, and Jing Gao. 2021. Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1618–1630, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (Wang et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.findings-emnlp.139.pdf
- Data
- CoNLL 2003