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(Author / Reviewer / Editor)
Zhang LY, Tan X, Kong F et al. Top-down text-level discourse rhetorical structure parsing with bidirectional representation learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(5): 985−1001 Sept. 2023. DOI: 10.1007/s11390-022-1167-0.
Citation: Zhang LY, Tan X, Kong F et al. Top-down text-level discourse rhetorical structure parsing with bidirectional representation learning. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(5): 985−1001 Sept. 2023. DOI: 10.1007/s11390-022-1167-0.

Top-down Text-Level Discourse Rhetorical Structure Parsing with Bidirectional Representation Learning

Funds: The work was supported by the National Natural Science Foundation of China under Grant No. 62276178.
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  • Author Bio:

    Long-Yin Zhang received his B.S. degree in software engineering from Jiangsu University of Science and Technology, Zhenjiang, in 2017. He is a Ph.D. candidate in computer science and technology at Soochow University, Suzhou. His current research interest lies in natural language understanding and discourse analysis

    Xin Tan received her B.S. degree in computer science and technology from Heilongjiang University, Harbin, in 2017. She is now a Ph.D. candidate in software engineering at Soochow University, Suzhou. Her current research interests include machine translation and natural language processing

    Fang Kong received her Ph.D. degree in computer science from the School of Computer Science and Technology at Soochow University, Suzhou, in 2009. She worked as a postdoctoral research fellow at the National University of Singapore, Kent Ridge, between 2011 and 2013. Currently, she is a full professor of the School of Computer Science and Technology at Soochow University, Suzhou. Her research interests include knowledge graph, discourse analysis, and natural language processing

    Pei-Feng Li received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2006. Currently, he is a professor in Soochow University, Suzhou. His current research interests include information extraction, machine learning, and natural language understanding

    Guo-Dong Zhou received his Ph.D. degree in computer science from National University of Singapore, Kent Ridge, in 1999. He is a distinguished professor in Soochow University, Suzhou. His research interests include information extraction, machine learning, and natural language processing

  • Corresponding author:

    kongfang@suda.edu.cn

  • Received Date: November 19, 2020
  • Accepted Date: November 20, 2022
  • Early studies on discourse rhetorical structure parsing mainly adopt bottom-up approaches, limiting the parsing process to local information. Although current top-down parsers can better capture global information and have achieved particular success, the importance of local and global information at various levels of discourse parsing is different. This paper argues that combining local and global information for discourse parsing is more sensible. To prove this, we introduce a top-down discourse parser with bidirectional representation learning capabilities. Existing corpora on Rhetorical Structure Theory (RST) are known to be much limited in size, which makes discourse parsing very challenging. To alleviate this problem, we leverage some boundary features and a data augmentation strategy to tap the potential of our parser. We use two methods for evaluation, and the experiments on the RST-DT corpus show that our parser can primarily improve the performance due to the effective combination of local and global information. The boundary features and the data augmentation strategy also play a role. Based on gold standard elementary discourse units (EDUs), our parser significantly advances the baseline systems in nuclearity detection, with the results on the other three indicators (span, relation, and full) being competitive. Based on automatically segmented EDUs, our parser still outperforms previous state-of-the-art work.

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