Papers › AMR Parsing as Sequence-to-Graph Transduction
AMR Parsing as Sequence-to-Graph Transduction
Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme
We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0 (70.2% F1 on LDC2014T12).
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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 | Two-stage Sequence-to-Graph Transducer | F1 Full | 70.2 | #5 of 12 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12: | Sequence-to-Graph Transduction | F1 Full | 0.70 | #1 of 5 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12: | Sequence-to-Graph Transduction | F1 Newswire | 0.75 | #1 of 5 | Archive leaderboard | report |
| AMR Parsing | LDC2017T10 | Sequence-to-Graph Transduction | Smatch | 76.3 | #21 of 27 | 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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