Decomposed Meta-Learning for Few-Shot Named Entity Recognition

• URL: http://arxiv.org/abs/2204.05751v2
• Date: Wed, 13 Apr 2022 14:08:35 GMT
• Title: Decomposed Meta-Learning for Few-Shot Named Entity Recognition
• Authors: Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, Chin-Yew Lin
• Abstract summary: Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. We present a meta-learning approach which tackles few-shot span detection and few-shot entity typing using meta-learning.
• Score: 32.515795881027074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the problem of few-shot NER by sequentially tackling few-shot span detection and few-shot entity typing using meta-learning. In particular, we take the few-shot span detection as a sequence labeling problem and train the span detector by introducing the model-agnostic meta-learning (MAML) algorithm to find a good model parameter initialization that could fast adapt to new entity classes. For few-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhanced prototypical networks to find a good embedding space that can better distinguish text span representations from different entity classes. Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods.

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