Abstract
The theory of discourse functional pragmatics posits that every discourse unit serves a purpose, and functional pragmatics can reveal its role in the text. To achieve a more informative representation of paragraphs, mitigate the long-tail problem, and incorporate the specificity of different models, we propose a Chinese discourse Functional Pragmatics Recognition model based on Multi-level information and Ensemble learning (FPRME). Specifically, two different encoding methods are combined to obtain paragraph representation that contains rich word-level information. Then, an encoder module is used to perform paragraph interactions, and graph convolutional networks are applied to enhance the interaction between paragraphs with dependencies. After that, a deep differential amplifier is applied to amplify the difference between paragraphs, and a weighted loss function is used to balance the attention of the categories. In addition, text augmentation and adversarial training are also employed to enhance the focus on minorities. Finally, ensemble learning is used to further improve the model’s performance. The experimental results on MCDTB 2.0 demonstrate that our model FPRME outperforms the state-of-the-art models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chu, X., Xi, X., Jiang, F., Xu, S., Zhu, Q., Zhou, G.: Research of macro discourse structure representation schema and resource construction. J. Softw. 31(2), 321–343 (2019)
van Dijk, T.A.: News as Discourse. University of Groningen (1988)
Jiang, F., Xu, S., Chu, X., Li, P., Zhu, Q., Zhou, G.: MCDTB: a macro-level Chinese discourse treebank. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3493–3504 (2018)
Du, M., Jiang, F., Chu, X., Li, P., Kong, F.: Discourse functional pragmatics recognition based on global semantic information and structural features. J. Softw. 36(11), 50–59 (2022)
Yarlott, W.V., Cornelio, C., Gao, T., Finlayson, M.: Identifying the discoursefunction of news article paragraphs. In: Proceedings of the Workshop Events and Stories in the News 2018, pp. 25–33 (2018)
Banisakher, D., Yarlott, W.V., Aldawsari, M., Rishe, N., Finlayson, M.: Improving the identification of the discourse function of news article paragraphs. In: Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pp. 17–25 (2020)
Choubey, P.K., Lee, A., Huang, R., Wang, L.: Discourse as a function of event: profiling discourse structure in news articles around the main event. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5374–5386 (2020)
Zhang, B., Choubey, P.K., Huang, R.: Predicting sentence deletions for text simplification using a functional discourse structure. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 255–261 (2022)
Song, W., Song, Z., Fu, R., Liu, L., Cheng, M., Liu, T.: Discourse self-attention for discourse element identification in argumentative student essays. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 2820–2830 (2020)
Choubey, P.K., Huang, R.: Profiling news discourse structure using explicit subtopic structures guided critics. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1594–1605 (2021)
Du, M., Jiang, F., Chu, X., Li, P.: Discourse functional pragmatics recognition based on news schemata. In: Proceedings of the 21st Chinese National Conference on Computational Linguistics, pp. 120–131 (2022)
Lu, Y., Fan, Y., Li, P., Chu, X., Zhu, Q.: Recognizing functional pragmatics in Chinese discourses on enhancing paragraph representation and deep differential amplifier. In: International Conference on Intelligent Computing, pp. 484–496 (2023)
Lu, Y., Jiang, F., Chu, X., Li, P., Zhu, Q.: Recognizing functional pragmatics of chinese discourses on data augmentation and dependency graph. In: International Conference on Intelligent Computing, pp. 523–535 (2023)
Xiao, W., Huber, P., Carenini, G.: Do we really need that many parameters in transformer for extractive summarization? Discourse can help! In: Proceedings of the First Workshop on Computational Approaches to Discourse, pp. 124–134 (2020)
Xu, J., Gan, Z., Cheng, Y., Liu, J.: Discourse-aware neural extractive text summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5021–5031 (2020)
Huang, Y.J., Kurohashi, S.: Extractive summarization considering discourse and coreference relations based on heterogeneous graph. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pp. 3046–3052 (2021)
Sun, Y., Yu, N., Fu, G.: A discourse-aware graph neural network for emotion recognition in multi-party conversation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2949–2958 (2021)
Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 6382–6388 (2019)
Karimi, A., Rossi, L., Prati, A.: AEDA: an easier data augmentation technique for text classification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2748–2754 (2021)
Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 86–96 (2016)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: Proceedings of the 6th International Conference on Learning Representations (2018)
Zhou, K., Zhao, W.X., Wang, S., Zhang, F., Wu, W., Wen, J.-R.: Virtual data augmentation: a robust and general framework for fine-tuning pre-trained models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3875–3887 (2021)
Jiao, X., et al.: TinyBERT: distilling bert for natural language understanding. In: Proceedings of the 2020Conference on Empirical Methods in Natural Language Processing, pp. 4163–4174 (2020)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 5753–5763 (2019)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31th International Conference on Neural Information Processing Systems, pp. 5998–6008 (2017)
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (2018)
Acknowledgments
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. 62376181 and 62276177), and Key R&D Plan of Jiangsu Province (BE2021048).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, Y., Fan, Y., Chu, X., Li, P., Zhu, Q. (2024). Recognizing Functional Pragmatics of Chinese Discourses on Multi-level Information and Ensemble Learning. In: Huang, DS., Si, Z., Zhang, C. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14878. Springer, Singapore. https://doi.org/10.1007/978-981-97-5672-8_31
Download citation
DOI: https://doi.org/10.1007/978-981-97-5672-8_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5671-1
Online ISBN: 978-981-97-5672-8
eBook Packages: Computer ScienceComputer Science (R0)