  Neural attention models have achieved great success in different NLP tasks.
How- ever, they have not fulfilled their promise on the AMR parsing task due to
the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence
model for AMR parsing and present different ways to tackle the data sparsity
problem. We show that our methods achieve significant improvement over a
baseline neural atten- tion model and our results are also compet- itive
against state-of-the-art systems that do not use extra linguistic resources.
