mlsanityData to 14 Oct 2026

Graph-to-Sequence Learning using Gated Graph Neural Networks

arXiv 1806.09835 ↗First posted 26 Jun 2018cs.CL cs.LGcited by 61

Not on the map. Why?

Abstract (arXiv)

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.

Key facts. Cited by 61 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2020: 1; 2021: 29; 2022: 16; 2023: 8; 2024: 3; 2025: 4.

61 papers.

Sort:

By area:

  1. 2021Graph Learning: A Surveycited by 141
  2. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  3. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  4. 2021Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualizationcited by 52
  5. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  6. 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
  7. 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
  8. 2023Towards Understanding the Generalization of Graph Neural Networkscited by 41
  9. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  10. 2021Distilling Holistic Knowledge with Graph Neural Networkscited by 26
  11. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  12. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 19
38 more
  1. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18
  2. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  3. 2021GraphFM: Graph Factorization Machines for Feature Interaction Modelingcited by 13
  4. 2021GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognitioncited by 12
  5. 2021Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidancecited by 12
  6. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  7. 2021Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reorderingcited by 11
  8. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  9. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  10. 2022GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimationcited by 9
  11. 2022Better Smatch = Better Parser? AMR evaluation is not so simple anymorecited by 9
  12. 2023AGI for Agriculturecited by 9
  13. 2025AutoG: Towards automatic graph construction from tabular datacited by 7
  14. 2021DyGCN: Dynamic Graph Embedding with Graph Convolutional Networkcited by 6
  15. 2021Graph Neural Network Based VC Investment Success Predictioncited by 6
  16. 2022Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentationcited by 6
  17. 2021Subjective Bias in Abstractive Summarizationcited by 5
  18. 2021Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detectioncited by 5
  19. 2022Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representationcited by 5
  20. 2024Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligencecited by 5
  21. 2021Fact-driven Logical Reasoning for Machine Reading Comprehensioncited by 4
  22. 2022Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Taggingcited by 4
  23. 2021Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generationcited by 3
  24. 2024Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Modelcited by 3
  25. 2021Literature review on vulnerability detection using NLP technologycited by 2
  26. 2021Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Modelscited by 2
  27. 2021Levi Graph AMR Parser using Heterogeneous Attentioncited by 2
  28. 2021Boosting Graph Search with Attention Network for Solving the General Orienteering Problemcited by 2
  29. 2022Graph-to-Text Generation with Dynamic Structure Pruningcited by 2
  30. 2022Scene Graph Modification as Incremental Structure Expandingcited by 2
  31. 2022Graph Classification via Discriminative Edge Feature Learningcited by 2
  32. 2023Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapterscited by 2
  33. 2024GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanismcited by 2
  34. 2021R2D2: Relational Text Decoding with Transformerscited by 1
  35. 2021Generating Diverse Descriptions from Semantic Graphscited by 1
  36. 2021Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networkscited by 1
  37. 2022Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networkscited by 1
  38. 2023Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphscited by 1

50 most cited of 61 All 61 (JSON)