@inproceedings{zhao-etal-2022-improving,
title = "Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation",
author = "Zhao, Yingxiu and
Tian, Zhiliang and
Yao, Huaxiu and
Zheng, Yinhe and
Lee, Dongkyu and
Song, Yiping and
Sun, Jian and
Zhang, Nevin",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.44",
doi = "10.18653/v1/2022.acl-long.44",
pages = "583--595",
abstract = "Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model{'}s reliance on support sets for task adaptation. Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of support sets stored in the memory. A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.",
}
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<abstract>Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model’s reliance on support sets for task adaptation. Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of support sets stored in the memory. A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.</abstract>
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%0 Conference Proceedings
%T Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
%A Zhao, Yingxiu
%A Tian, Zhiliang
%A Yao, Huaxiu
%A Zheng, Yinhe
%A Lee, Dongkyu
%A Song, Yiping
%A Sun, Jian
%A Zhang, Nevin
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhao-etal-2022-improving
%X Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model’s reliance on support sets for task adaptation. Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of support sets stored in the memory. A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.
%R 10.18653/v1/2022.acl-long.44
%U https://aclanthology.org/2022.acl-long.44
%U https://doi.org/10.18653/v1/2022.acl-long.44
%P 583-595
Markdown (Informal)
[Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation](https://aclanthology.org/2022.acl-long.44) (Zhao et al., ACL 2022)
ACL
- Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, and Nevin Zhang. 2022. Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 583–595, Dublin, Ireland. Association for Computational Linguistics.