  This work presents a novel approach to Automatic Post-Editing (APE) and
Word-Level Quality Estimation (QE) using ensembles of specialized Neural
Machine Translation (NMT) systems. Word-level features that have proven
effective for QE are included as input factors, expanding the representation of
the original source and the machine translation hypothesis, which are used to
generate an automatically post-edited hypothesis. We train a suite of NMT
models that use different input representations, but share the same output
space. These models are then ensembled together, and tuned for both the APE and
the QE task. We thus attempt to connect the state-of-the-art approaches to APE
and QE within a single framework. Our models achieve state-of-the-art results
in both tasks, with the only difference in the tuning step which learns weights
for each component of the ensemble.
