  Abstract Meaning Representation (AMR) annotation efforts have mostly focused
on English. In order to train parsers on other languages, we propose a method
based on annotation projection, which involves exploiting annotations in a
source language and a parallel corpus of the source language and a target
language. Using English as the source language, we show promising results for
Italian, Spanish, German and Chinese as target languages. Besides evaluating
the target parsers on non-gold datasets, we further propose an evaluation
method that exploits the English gold annotations and does not require access
to gold annotations for the target languages. This is achieved by inverting the
projection process: a new English parser is learned from the target language
parser and evaluated on the existing English gold standard.
