# AMR-to-Text Generation with Cache Transition Systems

arXiv: https://arxiv.org/abs/1912.01682 · HTML: https://mlsanity.com/paper/1912.01682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations](https://mlsanity.com/paper/2203.09711/) (2022)
- [MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2409.10294/) (2024)

## Abstract (arXiv)

Text generation from AMR involves emitting sentences that reflect the meaning of their AMR annotations. Neural sequence-to-sequence models have successfully been used to decode strings from flattened graphs (e.g., using depth-first or random traversal). Such models often rely on attention-based decoders to map AMR node to English token sequences. Instead of linearizing AMR, we directly encode its graph structure and delegate traversal to the decoder. To enforce a sentence-aligned graph traversal and provide local graph context, we predict transition-based parser actions in addition to English words. We present two model variants: one generates parser actions prior to words, while the other interleaves actions with words.