  Structured semantic sentence representations such as Abstract Meaning
Representations (AMRs) are potentially useful in various NLP tasks. However,
the quality of automatic parses can vary greatly and jeopardizes their
usefulness. This can be mitigated by models that can accurately rate AMR
quality in the absence of costly gold data, allowing us to inform downstream
systems about an incorporated parse's trustworthiness or select among different
candidate parses.
  In this work, we propose to transfer the AMR graph to the domain of images.
This allows us to create a simple convolutional neural network (CNN) that
imitates a human judge tasked with rating graph quality. Our experiments show
that the method can rate quality more accurately than strong baselines, in
several quality dimensions. Moreover, the method proves to be efficient and
reduces the incurred energy consumption.
