Research map / Graph learning / Graph self-supervised learning
Graph Transformer for Graph-to-Sequence Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a relation-aware global-attention Graph Transformer instead of the strongest reported baseline | AMR-to-text generation · LDC2015E86 | BLEU: 25.7 -> 27.4 | Helped |
| Use a relation-aware global-attention Graph Transformer instead of the strongest reported baseline | AMR-to-text generation · LDC2017T10 | BLEU: 27.6 -> 29.8 | Helped |
| Use a relation-aware global-attention Graph Transformer instead of the strongest reported baseline, including ensembles | Syntax-based machine translation · English-German | BLEU: 20.5 -> 21.3 | Helped |
| Use a relation-aware global-attention Graph Transformer instead of the strongest reported baseline, including ensembles | Syntax-based machine translation · English-Czech | BLEU: 13.1 -> 14.1 | Helped |
Closest papers by meaning
- Flatten Graphs as Sequences: Transformers are Scalable Graph Generators (NeurIPS 2025 · similarity 0.87)
- Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning (ICML 2024 · similarity 0.86)
- Learning Long Range Dependencies on Graphs via Random Walks (ICLR 2025 · similarity 0.85)
- Generative Graph Pattern Machine (NeurIPS 2025 · similarity 0.85)
- GOFA: A Generative One-For-All Model for Joint Graph Language Modeling (ICLR 2025 · similarity 0.85)
- UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains (NeurIPS 2025 · similarity 0.85)
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