Similarity-Driven Adaptive Prototypical Network for Class-incremental Few-shot Named Entity Recognition

Class-incremental Few-shot Named Entity Recognition (CFNER) aims to learn novel entity categories step by step and keep recognizing old classes simultaneously, in which only a few examples of novel classes are added at each incremental step. Many previous works have proved that decoupled two-phase (entity span detection and entity class discrimination) NER models are more suitable for handling CFNER. However, we find that in the second phase, discriminating entity spans has a large performance loss due to feature overlapping (i.e., samples of different categories appear relatively densely in the same region of the feature space). To solve this problem, we propose a Similarity-Driven Adaptive Prototypical Network (SDAPN) for enhancing current CFNER models. Specifically, we reserve a part of feature space for novel categories at the previous step and further mitigate the bias brought by anomalous samples according to the relative similarity of new samples and old class prototypes. Experimental results on two NER datasets show that our proposed approach significantly outperforms prior state-of-the-art approaches. A serial of analytical experiments is conducted to verify the effectiveness of our SDAPN model.