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

• URL: http://arxiv.org/abs/2211.03270v1
• Date: Mon, 7 Nov 2022 02:33:45 GMT
• Title: Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
• Authors: Youcheng Huang, Wenqiang Lei, Jie Fu and Jiancheng Lv
• Abstract summary: We propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds. Our work also provides an analytical viewpoint for addressing the general problems in few-shot name entity recognition.
• Score: 35.34238362639678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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