@inproceedings{de-lichy-etal-2021-meta,
title = "Meta-Learning for Few-Shot Named Entity Recognition",
author = "de Lichy, Cyprien and
Glaude, Hadrien and
Campbell, William",
editor = "Lee, Hung-Yi and
Mohtarami, Mitra and
Li, Shang-Wen and
Jin, Di and
Korpusik, Mandy and
Dong, Shuyan and
Vu, Ngoc Thang and
Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.metanlp-1.6",
doi = "10.18653/v1/2021.metanlp-1.6",
pages = "44--58",
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.",
}
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%0 Conference Proceedings
%T Meta-Learning for Few-Shot Named Entity Recognition
%A de Lichy, Cyprien
%A Glaude, Hadrien
%A Campbell, William
%Y Lee, Hung-Yi
%Y Mohtarami, Mitra
%Y Li, Shang-Wen
%Y Jin, Di
%Y Korpusik, Mandy
%Y Dong, Shuyan
%Y Vu, Ngoc Thang
%Y Hakkani-Tur, Dilek
%S Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F de-lichy-etal-2021-meta
%X 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.
%R 10.18653/v1/2021.metanlp-1.6
%U https://aclanthology.org/2021.metanlp-1.6
%U https://doi.org/10.18653/v1/2021.metanlp-1.6
%P 44-58
Markdown (Informal)
[Meta-Learning for Few-Shot Named Entity Recognition](https://aclanthology.org/2021.metanlp-1.6) (de Lichy et al., MetaNLP 2021)
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.