Top-Down RST Parsing Utilizing Granularity Levels in Documents

3 Apr 2020  ·  Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata ·

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.

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Results from the Paper


Ranked #3 on Discourse Parsing on RST-DT (RST-Parseval (Span) metric)

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Discourse Parsing RST-DT Top-down Span-based Parser RST-Parseval (Span) 87.0 # 3
RST-Parseval (Nuclearity) 74.6 # 4
RST-Parseval (Relation) 60.0 # 5

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