  The field of machine translation has progressed tremendously in recent years.
Even though the translation quality has improved significantly, current systems
are still unable to produce uniformly acceptable machine translations for the
variety of possible use cases. In this work, we put machine translation in a
cross-lingual pipeline and introduce downstream tasks to define task-specific
acceptability of machine translations. This allows us to leverage parallel data
to automatically generate acceptability annotations on a large scale, which in
turn help to learn acceptability detectors for the downstream tasks. We conduct
experiments to demonstrate the effectiveness of our framework for a range of
downstream tasks and translation models.
