Abstract
Rhetoric is abundant and universal across different human languages. In this paper, we propose a novel curriculum learning integrated with meta-learning (CLML) model to address the task of general rhetorical identification. Specifically, we first leverage inter-category similarities to construct a dataset with curriculum characteristics for facilitating more natural easy-to-difficult learning process. Then we imitate human cognitive thinking that uses the query set in meta-learning to guide inductive network for inducing accurate class-level representations which are further improved by leveraging external class label knowledge into TapNet to construct a mapping function. Extensive experimental results demonstrate that our proposed model outperforms existing state-of-the-art models across four datasets consistently.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Notes
IFLYTEK: a Chinese text classifier encompassing multiple categories http://challenge.xfyun.cn.
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Acknowledgements
The authors would like to thank all anonymous reviewers for their valuable comments and suggestions which have significantly improved the quality and presentation of this paper. The works described in this paper are supported by the National Natural Science Foundation of China (62076158, 62106130, 62072294, 62306204), Natural Science Foundation of Shanxi Province, China (20210302124084, 202103021223267), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2021L284), and CCF-Zhipu AI Large Model Foundation of China (CCF-Zhipu202310).
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Wang, D., Li, Y., Wang, S. et al. Integrating curriculum learning with meta-learning for general rhetoric identification. Int. J. Mach. Learn. & Cyber. 15, 2411–2425 (2024). https://doi.org/10.1007/s13042-023-02038-7
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DOI: https://doi.org/10.1007/s13042-023-02038-7