Abstract
Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples. However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms. In this paper, we apply two meta-learning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF). We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation. Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline. In addition, we propose a novel combination of Prototypical Networks and Reptile.- Anthology ID:
- 2021.metanlp-1.6
- Volume:
- Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- MetaNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–58
- Language:
- URL:
- https://aclanthology.org/2021.metanlp-1.6
- DOI:
- 10.18653/v1/2021.metanlp-1.6
- Cite (ACL):
- Cyprien de Lichy, Hadrien Glaude, and William Campbell. 2021. Meta-Learning for Few-Shot Named Entity Recognition. In Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing, pages 44–58, Online. Association for Computational Linguistics.
- Cite (Informal):
- Meta-Learning for Few-Shot Named Entity Recognition (de Lichy et al., MetaNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.metanlp-1.6.pdf
- Data
- SGD