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        <title>Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification</title>
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    <abstract>Meta-learning has emerged as an effective approach for few-shot text classification. However, current studies fail to realize the importance of the semantic interaction between sentence features and neglect to enhance the generalization ability of the model to new tasks. In this paper, we integrate an adversarial network architecture into the meta-learning system and leverage cost-effective modules to build a novel few-shot classification framework named SaAML. Significantly, our approach can exploit the temporal convolutional network to encourage more discriminative representation learning and explore the attention mechanism to promote more comprehensive feature expression, thus resulting in better adaptation for new classes. Through a series of experiments on four benchmark datasets, we demonstrate that our new framework acquires considerable superiority over state-of-the-art methods in all datasets, increasing the performance of 1-shot classification and 5-shot classification by 7.15% and 2.89%, respectively.</abstract>
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%0 Conference Proceedings
%T Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification
%A Wang, Suhe
%A Liu, Xiaoyuan
%A Liu, Bo
%A Dong, Diwen
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F wang-etal-2022-sentence-aware
%X Meta-learning has emerged as an effective approach for few-shot text classification. However, current studies fail to realize the importance of the semantic interaction between sentence features and neglect to enhance the generalization ability of the model to new tasks. In this paper, we integrate an adversarial network architecture into the meta-learning system and leverage cost-effective modules to build a novel few-shot classification framework named SaAML. Significantly, our approach can exploit the temporal convolutional network to encourage more discriminative representation learning and explore the attention mechanism to promote more comprehensive feature expression, thus resulting in better adaptation for new classes. Through a series of experiments on four benchmark datasets, we demonstrate that our new framework acquires considerable superiority over state-of-the-art methods in all datasets, increasing the performance of 1-shot classification and 5-shot classification by 7.15% and 2.89%, respectively.
%U https://aclanthology.org/2022.coling-1.428
%P 4844-4852

Markdown (Informal)

[Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification](https://aclanthology.org/2022.coling-1.428) (Wang et al., COLING 2022)

• Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification (Wang et al., COLING 2022)

ACL

• Suhe Wang, Xiaoyuan Liu, Bo Liu, and Diwen Dong. 2022. Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4844–4852, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.