  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.
