Abstract
As a fundamental task of Natural Language Processing (NLP), discourse parsing has attracted more and more attention in the past decade. Previous studies mainly focus on tree construction, while for the EDU representation, most researchers just conduct a simple flat word-level representation. Structural information within EDU and relationships between EDUs, especially between non-adjacent EDUs, are largely ignored. In this paper, we propose a two-layer enhanced representation approach to better model the context of EDUs. For the bottom layer (i.e., intra-EDU), we use Graph Convolutional Network (GCN) to continuously update the representation of words according to existing dependency paths from the root to the leaves. For the upper layer (i.e., inter-EDU), we use Star-Transformer to connect non-adjacent EDUs by the delay node and thus incorporate global information. Experimental results on the CDTB corpus show that the proposed two-layer context-enhanced representation can contribute much to Chinese discourse parsing in neural architecture.
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Notes
- 1.
Sun and Kong [16] proposed the strict macro averaged \(F_1\) for Chinese discourse parsing, in which they first calculate the \(F_{1}\) value for each CDT and then average the \(F_{1}\) values for all CDTs. Meanwhile, the correct determination of each internal node is more strict, not only the left and right boundaries are correctly recognized but also the split point for its childs is correct.
- 2.
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Acknowledgements
The authors would like to thank the anonymous reviewers for the helpful comments. This work was supported by Projects 61876118 under the National Natural Science Foundation of China, the National Key R &D Program of China under Grant No. 2020AAA0108600 and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Zhu, Q., Wang, K., Kong, F. (2022). Two-Layer Context-Enhanced Representation for Better Chinese Discourse Parsing. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https://doi.org/10.1007/978-3-031-17120-8_4
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