Meta-Learning for Few-Shot Named Entity Recognition
Cyprien de Lichy, Hadrien Glaude, William W. Campbell · 2021
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 metalearning 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.