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Most current state-of-the art systems for generating English text fromAbstract Meaning Representation (AMR) have been evaluated only using automatedmetrics, such as BLEU, which are known to be problematic for natural languagegeneration. In this work, we present the results of a new human evaluationwhich collects fluency and adequacy scores, as well as categorization of errortypes, for several recent AMR generation systems. We discuss the relativequality of these systems and how our results compare to those of automaticmetrics, finding that while the metrics are mostly successful in rankingsystems overall, collecting human judgments allows for more nuancedcomparisons. We also analyze common errors made by these systems.

A Human Evaluation of AMR-to-English Generation Systems