%I Association for Computational Linguistics %S Findings of the Association for Computational Linguistics: ACL 2022 %T Decomposed Meta-Learning for Few-Shot Named Entity Recognition Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods. 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. 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. 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. Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods.',ĭecomposed Meta-Learning for Few-Shot Named Entity Recognitionįindings of the Association for Computational Linguistics: ACL 2022Īssociation for Computational Linguisticsįew-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. Publisher = 'Association for Computational Linguistics',ĭoi = '10.18653/v1/2022.findings-acl.124',Ībstract = 'Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. Cite (Informal): Decomposed Meta-Learning for Few-Shot Named Entity Recognition (Ma et al., Findings 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Software:Ģ.zip Code microsoft/vert-papers Data CoNLL 2002, Few-NERD, WNUT = 'Decomposed Meta-Learning for Few-Shot Named Entity Recognition',īooktitle = 'Findings of the Association for Computational Linguistics: ACL 2022', Association for Computational Linguistics. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1584–1596, Dublin, Ireland.

Decomposed Meta-Learning for Few-Shot Named Entity Recognition. Anthology ID: 2022.findings-acl.124 Volume: Findings of the Association for Computational Linguistics: ACL 2022 Month: May Year: 2022 Address: Dublin, Ireland Venue: Findings SIG: Publisher: Association for Computational Linguistics Note: Pages: 1584–1596 Language: URL: DOI: 10.18653/v1/2022.findings-acl.124 Bibkey: ma-etal-2022-decomposed Cite (ACL): Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, and Chin-Yew Lin.

Abstract Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.

Meta learning maml