Papers › A Two-Stage Parsing Method for Text-Level Discourse Analysis
A Two-Stage Parsing Method for Text-Level Discourse Analysis
Yizhong Wang, Sujian Li, Houfeng Wang
Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Discourse Parsing | RST-DT | Two-stage Parser | RST-Parseval (Nuclearity) | 72.4 | #32 of 40 | Archive leaderboard | report |
| Discourse Parsing | RST-DT | Two-stage Parser | RST-Parseval (Relation) | 59.7 | #32 of 40 | Archive leaderboard | report |
| Discourse Parsing | RST-DT | Two-stage Parser | RST-Parseval (Span) | 86.0 | #32 of 40 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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