@inproceedings{wenjing-etal-2021-improving,
    title = "Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising",
    author = "Wenjing, Zhu  and
      Jian, Liu  and
      Jinan, Xu  and
      Yufeng, Chen  and
      Yujie, Zhang",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.ccl-1.101/",
    pages = "1131--1142",
    language = "eng",
    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 PLMto generate diverse training instances through predicting different masked words and design atask-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 of3.46{\%} F1 score on the 1{\%} CoNLL-2003 2.58{\%} on the 1{\%} OntoNotes5.0 and 0.99{\%} on the full of MaScip."
}