@inproceedings{ma2022decomposed,
author = {Ma, Tingting and Jiang, Huiqiang and Wu, Qianhui and Zhao, Tiejun and Lin, Chin-Yew},
title = {Decomposed Meta-Learning for Few-Shot Named Entity Recognition},
booktitle = {60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) | Findings},
year = {2022},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/decomposed-meta-learning-for-few-shot-named-entity-recognition/},
}