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Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition by Huang, Youcheng; Lei, Wenqiang; Fu, Jie; Lv, Jiancheng is a scholarly article available to read on EtoBox.

What is Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition about?

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 state-of-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.

Author

Huang, Youcheng; Lei, Wenqiang; Fu, Jie; Lv, Jiancheng

Published

2022

Language

EN