Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Youcheng Huang, Wenqiang Lei, Jie Fu, Jiancheng Lv · 2022
Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition.Existing methods, unfortunately, are not aware of the fact that embeddings from pre-trained models contain a prominently large amount of information regarding word frequencies, biasing prototypical neural networks against learning word entities.This discrepancy constrains the two models' synergy.Thus, we propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds.Our experiments based on nine benchmark datasets show the superiority of our method over the counterpart models and are comparable to the stateof-the-art methods.In addition to the model enhancement, our work also provides an analytical viewpoint for addressing the general problems in few-shot name entity recognition or other tasks that rely on pre-trained models or prototypical neural networks.1