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Applications and Challenges of Meta-Learning in Natural Language Processing
- 1 School of Engineering, Virginia Polytechnic Institute and State University, VA, USA
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Abstract
With the emergence and development of meta-learning algorithms, their remarkable advantages in few-shot learning (FSL) scenarios have led to the increasing application of meta-learning in natural language processing (NLP). However, meta-learning is not without limitations, and various challenges may arise during its practical application. This paper begins by providing a brief introduction to the concept of meta-learning and delves into its complex applications in NLP, such as federated learning (FL), cross-task generalization, and few-shot text classification. The research demonstrates that the advantages of meta-learning in few-shot learning can significantly aid these applications, particularly in scenarios where data scarcity necessitates rapid adaptation. Nevertheless, throughout these studies, meta-learning also exhibits limitations in various contexts. Finally, this paper summarizes the content, analyzes the interrelationships and potential connections between the topics, and highlights current challenges in the field as well as possible future directions for development.
Keywords
Meta-Learning, Few-Shot Learning, Federated learning, Cross-task Generalization, Few-Shot Text Classification.
[1]. Vettoruzzo, A., Bouguelia, M.-R., Vanschoren, J., Rognvaldsson, T., & Santosh, K. (2024). Advances and challenges in meta-learning: A technical review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7), 4763–4779. https://doi.org/10.1109 /TPAMI.2024.3357847
[2]. Sherborne, T., & Lapata, M. (2023). Meta-learning a cross-lingual manifold for semantic parsing. Transactions of the Association for Computational Linguistics, 11, 49-67. https://doi.org/10.1162/tacl_a_00533
[3]. Wang, W., Wang, Y., Hoi, S. C. H., & Joty, S. (2023). Towards low-resource automatic program repair with meta-learning and pretrained language models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 6954–6968. https://doi.org/10.18653/v1/2023.emnlp-main.598
[4]. Huisman, M., van Rijn, J. N., & Plaat, A. (2021). A survey of deep meta-learning. Artificial Intelligence Review, 54(5), 4483–4541. https://doi.org/10.1007/s10462-021-10004-4
[5]. Jankowski, N., Duch, W., & Grabczewski, K. (2011). Meta-learning in computational intelligence (1st ed. 2011). Springer. https://doi.org/10.1007/978-3-642-20980-2
[6]. Singh, A., Chandrasekar, S., Saha, S., & Sen, T. (2023). Federated meta-learning for emotion and sentiment aware multi-modal complaint identification. Proceedings of the EMNLP 2023.
[7]. Qin, C., Joty, S., Li, Q., & Zhao, R. (2024). Learning to initialize: Can meta-learning improve cross-task generalization in prompt tuning? Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 11802–11809.
[8]. Li, X., Song, K., Lin, T., Kang, Y., Zhao, F., Sun, C., & Liu, X. (2024). PDAMeta: Meta-learning framework with progressive data augmentation for few-shot text classification. Proceedings of the LREC-COLING 2024, 12668–12678.
Cite this article
Luo,S. (2024). Applications and Challenges of Meta-Learning in Natural Language Processing. Applied and Computational Engineering,109,1-8.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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