  We propose an attention-based model that treats AMR parsing as
sequence-to-graph transduction. Unlike most AMR parsers that rely on
pre-trained aligners, external semantic resources, or data augmentation, our
proposed parser is aligner-free, and it can be effectively trained with limited
amounts of labeled AMR data. Our experimental results outperform all previously
reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0
(70.2% F1 on LDC2014T12).
