Papers › Broad-Coverage Semantic Parsing as Transduction
Broad-Coverage Semantic Parsing as Transduction
Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme
We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| AMR Parsing | LDC2014T12 | Broad-Coverage Semantic Parsing as Transduction | F1 Full | 71.3 | #4 of 12 | Archive leaderboard | report |
| AMR Parsing | LDC2017T10 | Zhang et al. | Smatch | 77.0 | #20 of 27 | Archive leaderboard | report |
| UCCA Parsing | SemEval 2019 Task 1 | Neural Transducer | English-Wiki (open) F1 | 76.6 | #2 of 4 | 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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