# AMR-to-text Generation with Synchronous Node Replacement Grammar

arXiv: https://arxiv.org/abs/1702.00500 · HTML: https://mlsanity.com/paper/1702.00500/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis](https://mlsanity.com/paper/2508.10013/) (2025)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Latent Tree Decomposition Parsers for AMR-to-Text Generation](https://mlsanity.com/paper/2108.12304/) (2021)
- [A Survey : Neural Networks for AMR-to-Text](https://mlsanity.com/paper/2206.07328/) (2022)

## 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.