Abstract
Most previous studies used various sequence learning models to represent discourse arguments, which not only limit the model to perceive global information, but also make it difficult to deal with long-distance dependencies when the discourse arguments are paragraph-level or document-level. To address the above issues, we propose a GCN-based neural network model on discourse argument pair graph to transform discourse relation recognition into a node classification task. Specifically, we first convert discourse arguments of all samples into a heterogeneous text graph that integrates word-related global information and argument-related keyword information. Then, we use a graph learning method to encode argument semantics and recognize the relationship between arguments. The experimental results on the Chinese MCDTB corpus show that our proposed model can effectively recognize the discourse relations and outperforms the SOTA model.
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Acknowledgments
The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61836007, 61772354 and 61773276.), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Sun, Z., Jiang, F., Li, P., Zhu, Q. (2020). Macro Discourse Relation Recognition via Discourse Argument Pair Graph. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_9
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