AMR-to-text Generation with Synchronous Node Replacement Grammar
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Abstract (arXiv)
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which is the best reported so far.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 2.
5 papers.
- 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
- 2025Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesiscited by 1
- 2021Understanding in Artificial Intelligencecited by 0
- 2021Latent Tree Decomposition Parsers for AMR-to-Text Generationcited by 0
- 2022A Survey : Neural Networks for AMR-to-Textcited by 0
- 2025Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis6 Aug 2025
- 2025Survey of Abstract Meaning Representation: Then, Now, Future6 May 2025
- 2022A Survey : Neural Networks for AMR-to-Text15 Jun 2022
- 2021Latent Tree Decomposition Parsers for AMR-to-Text Generation27 Aug 2021
- 2021Understanding in Artificial Intelligence17 Jan 2021