Papers › Transition-based Neural RST Parsing with Implicit Syntax Features

Transition-based Neural RST Parsing with Implicit Syntax Features

1 Aug 2018COLING 2018 8archive 2025-07-28

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.

PaperPDFConference PDFCode

Code

fajri91/NeuralRST officialpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Discourse ParsingWord Embeddings

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections