Top-Down RST Parsing Utilizing Granularity Levels in Documents


  • Naoki Kobayashi Tokyo Institute of Technology
  • Tsutomu Hirao NTT Corporation
  • Hidetaka Kamigaito Tokyo Institute of Technology
  • Manabu Okumura Tokyo Institute of Technology
  • Masaaki Nagata Tokyo Institute of Technology



Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity in a document, paragraphs, sentences and Elementary Discourse Units (EDUs), to parse a document accurately and efficiently. The parsing is done in a top-down manner for each granularity level, by recursively splitting a larger text span into two smaller ones while predicting nuclearity and relation labels for the divided spans. The results on the RST-DT corpus show that our method achieved the state-of-the-art results, 87.0 unlabeled span score, 74.6 nuclearity labeled span score, and the comparable result with the state-of-the-art, 60.0 relation labeled span score. Furthermore, discourse dependency trees converted from our RST trees also achieved the state-of-the-art results, 64.9 unlabeled attachment score and 48.5 labeled attachment score.




How to Cite

Kobayashi, N., Hirao, T., Kamigaito, H., Okumura, M., & Nagata, M. (2020). Top-Down RST Parsing Utilizing Granularity Levels in Documents. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8099-8106.



AAAI Technical Track: Natural Language Processing