Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising

Wenjing Zhu, Jian Liu, Jinan Xu, Yufeng Chen, Yujie Zhang

Abstract: Deep neural networks have achieved state-of-the-art performances on named entity recognition (NER) with sufficient training data, while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem, we propose a novel data augmentation method based on pre-trained language model (PLM) and curriculum learning strategy. Concretely, we use the PLM to generate diverse training instances through predicting different masked words and design a task-specific curriculum learning strategy to alleviate the influence of noises. We evaluate the effectiveness of our approach on three datasets: CoNLL-2003, OntoNotes5.0, and MaScip, of which the first two are simulated low-resource scenarios, and the last one is a real low-resource dataset in material science domain. Experimental results show that our method consistently outperform the baseline model. Specifically, our method achieves an absolute improvement of 3.46% $$F_1$$ score on the 1% CoNLL-2003, 2.58% on the 1% OntoNotes5.0, and 0.99% on the full of MaScip.