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Few-shot named entity recognition (NER) systems aim at recognizingnovel-class named entities based on only a few labeled examples. In this paper,we present a decomposed meta-learning approach which addresses the problem offew-shot NER by sequentially tackling few-shot span detection and few-shotentity typing using meta-learning. In particular, we take the few-shot spandetection as a sequence labeling problem and train the span detector byintroducing the model-agnostic meta-learning (MAML) algorithm to find a goodmodel parameter initialization that could fast adapt to new entity classes. Forfew-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhancedprototypical networks to find a good embedding space that can betterdistinguish text span representations from different entity classes. Extensiveexperiments on various benchmarks show that our approach achieves superiorperformance over prior methods.

Decomposed Meta-Learning for Few-Shot Named Entity Recognition