# Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation

arXiv: https://arxiv.org/abs/1504.06665 · HTML: https://mlsanity.com/paper/1504.06665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2015.

## Built on by (most cited first, 1 of 1)

- [Persian Abstract Meaning Representation: Annotation Guidelines and Gold Standard Dataset](https://mlsanity.com/paper/2205.07712/) (2022)

## Abstract (arXiv)

We present a parser for Abstract Meaning Representation (AMR). We treat English-to-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.