Abstract
In the past decade, Chinese Discourse Parsing has drawn much attention due to its fundamental role in document-level Natural Language Processing (NLP). In this work, we propose a topic-aware two-layer context-enhanced model based on transition system. Specifically, in one hand, we first adopt a two-layer context-enhanced Chinese discourse parser as a strong baseline, where the Star-Transformer with star topology is employed to enhance the EDU representation. On the other hand, we split the document into multiple sub-topics based on the change of nuclearity of discourse relations. Then we implicitly incorporate topic boundary information via joint learning framework. Experimental results on the Chinese CDTB corpus indicate that, the proposed approach can contribute much to Chinese discourse parsing.
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Acknowledgement
This work was supported by Projects 62276178 under the National Natural Science Foundation of China, the National Key RD Program of China under Grant No. 2020AAA0108600 and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Wang, K., Zhu, Q., Kong, F. (2024). Topic-Aware Two-Layer Context-Enhanced Model for Chinese Discourse Parsing. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_11
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