  As alignment links are not given between English sentences and Abstract
Meaning Representation (AMR) graphs in the AMR annotation, automatic alignment
becomes indispensable for training an AMR parser. Previous studies formalize it
as a string-to-string problem and solve it in an unsupervised way, which
suffers from data sparseness due to the small size of training data for
English-AMR alignment. In this paper, we formalize it as a syntax-based
alignment problem and solve it in a supervised manner based on syntax trees,
which can address the data sparseness problem by generalizing English-AMR
tokens to syntax tags. Experiments verify the effectiveness of the proposed
method not only for English-AMR alignment, but also for AMR parsing.
