Few-Shot Class-Incremental Learning for Named Entity Recognition

By Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, S. Mitra, Ruiyi Zhang, Ricardo Henao. 2022

27 citations recorded by the source corpus.

Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, i.e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones. To alleviate the problem of catastrophic forgetting in few-shot class-incremental learning, we reconstruct synthetic training data of the old classes using the trained NER model, augmenting the training of new classes. We further develop a framework that distills from the existing model with both synthetic data, and real data from the current training set. Experimental results show that our approach achieves significant improvements over existing baselines.

Subfields: Information Extraction, Meta & Continual Learning

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Extraction

  1. Jun Zhu

In Meta & Continual Learning

  1. Xingxing Zhang
  2. Liyuan Wang
  3. Hang Su
  4. Jun Zhu
  5. Simone Scardapane
  6. Valerio Marsocci
  7. Liyuan Wang
  8. Hongwei Yan

Citations in the corpus