@inproceedings{dong-etal-2020-meta,
title = "Meta-Information Guided Meta-Learning for Few-Shot Relation Classification",
author = "Dong, Bowen and
Yao, Yuan and
Xie, Ruobing and
Gao, Tianyu and
Han, Xu and
Liu, Zhiyuan and
Lin, Fen and
Lin, Leyu and
Sun, Maosong",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.140",
doi = "10.18653/v1/2020.coling-main.140",
pages = "1594--1605",
abstract = "Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.",
}
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<abstract>Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.</abstract>
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%0 Conference Proceedings
%T Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
%A Dong, Bowen
%A Yao, Yuan
%A Xie, Ruobing
%A Gao, Tianyu
%A Han, Xu
%A Liu, Zhiyuan
%A Lin, Fen
%A Lin, Leyu
%A Sun, Maosong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F dong-etal-2020-meta
%X Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.
%R 10.18653/v1/2020.coling-main.140
%U https://aclanthology.org/2020.coling-main.140
%U https://doi.org/10.18653/v1/2020.coling-main.140
%P 1594-1605
Markdown (Informal)
[Meta-Information Guided Meta-Learning for Few-Shot Relation Classification](https://aclanthology.org/2020.coling-main.140) (Dong et al., COLING 2020)
ACL
- Bowen Dong, Yuan Yao, Ruobing Xie, Tianyu Gao, Xu Han, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2020. Meta-Information Guided Meta-Learning for Few-Shot Relation Classification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1594–1605, Barcelona, Spain (Online). International Committee on Computational Linguistics.