We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework. We evaluate our parser on multiple datasets from varied domains and show an absolute improvement of 2% to 6% over the state-of-the-art. Additionally we show that using the most frequent concept gives us a baseline that is stronger than the state-of-the-art for concept prediction. We plan to release our parser for public use.
@article{arxiv.1510.07586,
title = {Parser for Abstract Meaning Representation using Learning to Search},
author = {Sudha Rao and Yogarshi Vyas and Hal Daume and Philip Resnik},
journal= {arXiv preprint arXiv:1510.07586},
year = {2015}
}