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    <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.</abstract>
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%0 Conference Proceedings
%T Decomposed Meta-Learning for Few-Shot Named Entity Recognition
%A Ma, Tingting
%A Jiang, Huiqiang
%A Wu, Qianhui
%A Zhao, Tiejun
%A Lin, Chin-Yew
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ma-etal-2022-decomposed
%X 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.
%R 10.18653/v1/2022.findings-acl.124
%U https://aclanthology.org/2022.findings-acl.124
%U https://doi.org/10.18653/v1/2022.findings-acl.124
%P 1584-1596

Markdown (Informal)

[Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://aclanthology.org/2022.findings-acl.124) (Ma et al., Findings 2022)

• Decomposed Meta-Learning for Few-Shot Named Entity Recognition (Ma et al., Findings 2022)

ACL

• Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, and Chin-Yew Lin. 2022. Decomposed Meta-Learning for Few-Shot Named Entity Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1584–1596, Dublin, Ireland. Association for Computational Linguistics.