Few-Shot Class-Incremental Learning for Named Entity Recognition

Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

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 generate synthetic 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 NER model from previous steps with both synthetic data, and real data from the current training set. Experimental results show that our approach achieves significant improvements over existing baselines.
Original languageEnglish (US)
Title of host publicationProceedings of the Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics (ACL)
Pages571-582
Number of pages12
ISBN (Print)9781955917216
StatePublished - Jan 1 2022
Externally publishedYes

Fingerprint

Dive into the research topics of 'Few-Shot Class-Incremental Learning for Named Entity Recognition'. Together they form a unique fingerprint.

Cite this