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

Supervised Syntax-based Alignment between English Sentences and Abstract Meaning Representation Graphs