Learning to Generalize Unseen Domains via Multi-source Meta Learning for Text Classification
Hu, Yuxuan Zhang, Chenwei Yang, Min Liang, Xiaodan Li, Chengming Hu, Xiping
Hu, Yuxuan
Zhang, Chenwei
Yang, Min
Liang, Xiaodan
Li, Chengming
Hu, Xiping
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
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Journal Issue
Abstract
With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have achievedhigh accuracy. However, most of these models are trained using labeled data from seen domains. It is difficult for these models to maintain high accuracy in a new challenging unseen domain, which is directly related to the generalization of the model. In this paper, we study the multi-source Domain Generalization for text classification and propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain. Specifically, we propose a multi-source meta-learning Domain Generalization framework to simulate the process of model generalization to an unseen domain, so as to extract sufficient domain-related features. We introduce a memory mechanism to store domain-specific features, which coordinate with the meta-learning framework. Besides, we adopt a novel “jury” mechanism that enables the model to learn sufficient domain-invariant features. Experiments demonstrate that our meta-learning framework can effectively enhance the ability of the model to generalize to an unseen domain and can outperform the state-of-the-art methods on multi-source text classification datasets.
Citation
Y. Hu, C. Zhang, M. Yang, X. Liang, C. Li, and X. Hu, “Learning to Generalize Unseen Domains via Multi-source Meta Learning for Text Classification,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15319 LNCS, pp. 412–428, 2025, doi: 10.1007/978-3-031-78495-8_26.
Source
International Conference on Pattern Recognition
Conference
Keywords
Generalisation, High-accuracy, Labeled data, Learning methods, Memory mechanism, Meta-learning frameworks, Metalearning, Model generalization, Multi-Sources, Text classification
Subjects
Source
Publisher
Springer Nature