mlsanityData to 7 Oct 2026

Structural Information Preserving for Graph-to-Text Generation

arXiv 2102.06749 ↗First posted 12 Feb 2021cs.CLcited by 24

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Abstract (arXiv)

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline. Our code is available at urlhttp://github.com/Soistesimmer/AMR-multiview.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

34 of 52 references.

References · 34

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  3. 2017Graph Attention Networkscited by 8,854
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  6. 2015Gated Graph Sequence Neural Networkscited by 792
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  8. 2017Challenges in Data-to-Document Generationcited by 226
  9. 2019Text Generation from Knowledge Graphs with Graph Transformerscited by 122
  10. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  11. 2019Graph Transformer for Graph-to-Sequence Learningcited by 82
  12. 2019Semantic Noise Matters for Neural Natural Language Generationcited by 64
22 more
  1. 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
  2. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  3. 2018A Graph-to-Sequence Model for AMR-to-Text Generationcited by 56
  4. 2020GPT-too: A language-model-first approach for AMR-to-text generationcited by 38
  5. 2019Semantic Neural Machine Translation using AMRcited by 37
  6. 2019Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialoguecited by 35
  7. 2019Modeling Graph Structure in Transformer for Better AMR-to-Text Generationcited by 35
  8. 2018SQL-to-Text Generation with Graph-to-Sequence Modelcited by 31
  9. 2019Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learningcited by 29
  10. 2016Neural Machine Translation with Reconstructioncited by 28
  11. 2017Empower Sequence Labeling with Task-Aware Neural Language Modelcited by 28
  12. 2018Findings of the E2E NLG Challengecited by 28
  13. 2018Deep Graph Convolutional Encoders for Structured Data to Text Generationcited by 27
  14. 2019Enhancing AMR-to-Text Generation with Dual Graph Representationscited by 24
  15. 2017Semi-supervised Multitask Learning for Sequence Labelingcited by 20
  16. 2017Towards String-to-Tree Neural Machine Translationcited by 19
  17. 2019Structural Neural Encoders for AMR-to-text Generationcited by 19
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  19. 2018An AMR Aligner Tuned by Transition-based Parsercited by 12
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  21. 2018Jointly Learning to Label Sentences and Tokenscited by 9
  22. 2018Factorising AMR generation through syntaxcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 6; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

24 papers.

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  1. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  2. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128
  3. 2021JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphscited by 58
  4. 2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
  5. 2021Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Modelscited by 29
  6. 2021Semantic Representation for Dialogue Modelingcited by 24
  7. 2023Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphscited by 24
  8. 2023MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuningcited by 16
  9. 2021Constrained Text Generation with Global Guidance -- Case Study on CommonGencited by 10
  10. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  11. 2022Type-Driven Multi-Turn Corrections for Grammatical Error Correctioncited by 9
  12. 2021LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-trainingcited by 5
12 more
  1. 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directionscited by 5
  2. 2021Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderingscited by 4
  3. 2023Planning with Logical Graph-based Language Model for Instruction Generationcited by 4
  4. 2021VOGUE: Answer Verbalization through Multi-Task Learningcited by 3
  5. 2022Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Informationcited by 3
  6. 2024Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluationcited by 2
  7. 2024GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanismcited by 2
  8. 2025DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusioncited by 2
  9. 2023Knowledge Graph for NLG in the context of conversational agentscited by 1
  10. 2022A Survey : Neural Networks for AMR-to-Textcited by 0
  11. 2023Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networkscited by 0
  12. 2026From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verificationcited by 0