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Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

arXiv 1704.08381 ↗First posted 26 Apr 2017cs.CLcited by 56

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Abstract (arXiv)

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text usingAbstract Meaning Representation (AMR)has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.

Key facts. Cited by 56 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Apr 2017.

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Citing papers per year: 2021: 20; 2022: 11; 2023: 10; 2024: 6; 2025: 8; 2026: 1.

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  1. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 152
  2. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  3. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  4. 2021AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Modelscited by 31
  5. 2021Maximum Bayes Smatch Ensemble Distillation for AMR Parsingcited by 29
  6. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  7. 2021Semantic Representation for Dialogue Modelingcited by 24
  8. 2021Improving Compositional Generalization with Self-Training for Data-to-Text Generationcited by 19
  9. 2023AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Modelcited by 19
  10. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  11. 2021Ensembling Graph Predictions for AMR Parsingcited by 15
  12. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
38 more
  1. 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
  2. 2023Sequential Query Encoding For Complex Query Answering on Knowledge Graphscited by 13
  3. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  4. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  5. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  6. 2023Improving Medical Dialogue Generation with Abstract Meaning Representationscited by 10
  7. 2021Bootstrapping Multilingual AMR with Contextual Word Alignmentscited by 8
  8. 2021Multilingual AMR Parsing with Noisy Knowledge Distillationcited by 8
  9. 2022A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Ratingcited by 5
  10. 2022Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representationscited by 4
  11. 2024A Systematic Review of Data-to-Text NLGcited by 4
  12. 2024Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLMcited by 4
  13. 2021One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasetscited by 3
  14. 2021Targeted Data Acquisition for Evolving Negotiation Agentscited by 3
  15. 2021Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generationcited by 3
  16. 2022Semantic-based Pre-training for Dialogue Understandingcited by 3
  17. 2024Prompting for Numerical Sequences: A Case Study on Market Comment Generationcited by 3
  18. 2021Levi Graph AMR Parser using Heterogeneous Attentioncited by 2
  19. 2021StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representationscited by 2
  20. 2022SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representationcited by 2
  21. 2022Cross-domain Generalization for AMR Parsingcited by 2
  22. 2023Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantificationcited by 2
  23. 2023Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapterscited by 2
  24. 2024Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluationcited by 2
  25. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  26. 2021Making Better Use of Bilingual Information for Cross-Lingual AMR Parsingcited by 1
  27. 2021Generating Diverse Descriptions from Semantic Graphscited by 1
  28. 2023AMR Parsing with Causal Hierarchical Attention and Pointerscited by 1
  29. 2024Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parsercited by 1
  30. 2025Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesiscited by 1
  31. 2025A Theorem-Proving-Based Evaluation of Neural Semantic Parsingcited by 1
  32. 2021Tree Decomposition Attention for AMR-to-Text Generationcited by 0
  33. 2021Latent Tree Decomposition Parsers for AMR-to-Text Generationcited by 0
  34. 2021Self-Attentive Constituency Parsing for UCCA-based Semantic Parsingcited by 0
  35. 2022Transition-based Abstract Meaning Representation Parsing with Contextual Embeddingscited by 0
  36. 2022A Survey : Neural Networks for AMR-to-Textcited by 0
  37. 2022Strictly Breadth-First AMR Parsingcited by 0
  38. 2023Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistencycited by 0

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