Abstract
The study of discourse functional pragmatic structure attaches importance to the function of discourse units. Existing models have poor performance in the functional pragmatics recognition of minority categories and ignore discourse dependency structure to enhance the representation of discourse units. To address the above issues, we propose a Functional Pragmatic Recognition model based on Dependency Structure (FPRDS) to recognise the functional pragmatic structures of Chinese discourses. Specifically, we first propose a data augmentation approach based on adversarial training and subtree swapping to enhance the recognition performance of minority categories. And then we use graph convolutional networks to incorporate the discourse dependency information to better enhance the representations of discourse units. The experimental results show that our FPRDS outperforms the state-of-the-art models, especially for minority categories.
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Acknowledgements
The authors would like to thank the two anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (Nos. 62276177, and 61836007), and Project Funded by the Priority Aca-demic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Lu, Y., Jiang, F., Chu, X., Li, P., Zhu, Q. (2023). Recognizing Functional Pragmatics of Chinese Discourses on Data Augmentation and Dependency Graph. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_43
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