Papers › Transition-based Neural RST Parsing with Implicit Syntax Features
Transition-based Neural RST Parsing with Implicit Syntax Features
Nan Yu, Meishan Zhang, Guohong Fu
Syntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete output trees produced by a supervised syntax parser. In this paper, we propose an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser. In addition, we propose a simple transition-based model as the baseline, further enhancing it with dynamic oracle. Experiments on the standard dataset show that our baseline model with dynamic oracle is highly competitive. When implicit syntax features are integrated, we are able to obtain further improvements, better than using explicit Tree-RNN.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Discourse Parsing | RST-DT | Transition-based Parser with Implicit Syntax Features | RST-Parseval (Full) | 59.9 | #29 of 40 | Archive leaderboard | report |
| Discourse Parsing | RST-DT | Transition-based Parser with Implicit Syntax Features | RST-Parseval (Nuclearity) | 73.1 | #29 of 40 | Archive leaderboard | report |
| Discourse Parsing | RST-DT | Transition-based Parser with Implicit Syntax Features | RST-Parseval (Relation) | 60.2 | #29 of 40 | Archive leaderboard | report |
| Discourse Parsing | RST-DT | Transition-based Parser with Implicit Syntax Features | RST-Parseval (Span) | 85.5 | #29 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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