Papers › Graph Pre-training for AMR Parsing and Generation
Graph Pre-training for AMR Parsing and Generation
Xuefeng Bai, Yulong Chen, Yue Zhang
Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively. However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge. To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs. In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training. We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model. To our knowledge, we are the first to consider pre-training on semantic graphs.
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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 | Bio | AMRBART large | Smatch | 63.2 | #2 of 5 | Archive leaderboard | report |
| AMR Parsing | LDC2017T10 | AMRBART large | Smatch | 85.4 | #6 of 27 | Archive leaderboard | report |
| AMR Parsing | LDC2020T02 | AMRBART large | Smatch | 84.2 | #6 of 13 | Archive leaderboard | report |
| AMR Parsing | New3 | AMRBART large | Smatch | 76.9 | #1 of 4 | Archive leaderboard | report |
| AMR Parsing | The Little Prince | AMRBART large | Smatch | 79.8 | #1 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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