Abstract
The macro-level discourse parsing, as a fundamental task of macro discourse analysis, mainly focuses on converting a document into a hierarchical discourse tree at paragraph level. Most existing methods follow micro-level studies and suffer from the issues of semantic representation and the semantic interaction of the larger macro discourse units. Therefore, we propose a macro-level discourse parser based on the dependency graph convolutional network to enhance the semantic representation of the large discourse unit and the semantic interaction between those large discourse units. Experimental results on both the Chinese MCDTB and English RST-DT show that our model outperforms several state-of-the-art baselines.
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Acknowledgements
The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61773276, 61772354 and 61836007.), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Fan, Y., Jiang, F., Chu, X., Li, P., Zhu, Q. (2021). Chinese Macro Discourse Parsing on Dependency Graph Convolutional Network. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13028. Springer, Cham. https://doi.org/10.1007/978-3-030-88480-2_2
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DOI: https://doi.org/10.1007/978-3-030-88480-2_2
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