Papers › A Graph-to-Sequence Model for AMR-to-Text Generation
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph-to-Sequence | LDC2015E86: | GRN | BLEU | 33.6 | #1 of 2 | Archive leaderboard | report |
| Text Generation | LDC2016E25 | Graph2Seq | BLEU | 22 | #1 of 1 | Archive leaderboard | report |
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Methods
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