Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification

Suhe Wang, Xiaoyuan Liu, Bo Liu, Diwen Dong


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.
Anthology ID:
2022.coling-1.428
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4844–4852
Language:
URL:
https://aclanthology.org/2022.coling-1.428
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification (Wang et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.428.pdf