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Integrating curriculum learning with meta-learning for general rhetoric identification

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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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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. 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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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, China

    Dian Wang, Suge Wang, Shuqi Li, Jian Liao & Deyu Li

  2. School of Finance, Shanxi University of Finance and Economics, Taiyuan, China

    Yang Li

  3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, China

    Suge Wang & Deyu Li

  4. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Xiaoli Li

  5. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, China

    Xin Chen

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  1. Dian Wang
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Correspondence to Suge Wang.

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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. (2024). https://doi.org/10.1007/s13042-023-02038-7

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