Continual Few-Shot Learning for Named Entity Recognition

Previous work in continual learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data in the new datasets arriving over time. This assumption is usually unrealistic since the token-level annotations required by NER training are laborious and scarce, especially for new (unseen) classes. We present the first work to study continual few-shot learning for NER, which is more general, but as a result, more challenging, compared to continual learning for NER. To alleviate the problem of catastrophic forgetting in continual few-shot learning, we reconstruct synthetic training data of the previously seen classes from the NER model and further develop a framework that distills from the existing model with both synthetic data, and real data from the current training set. Experimental results on several NER benchmarks show that our approach achieves significant improvements over existing baselines.