In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs mt (raw MT output) and src
(source language input) in a single neural architecture, modeling {mt,src}→pe directly. Apart from that, we investigate the influence of
hard-attention models which seem to be well-suited for monolingual tasks, as
well as combinations of both ideas. We report results on data sets provided
during the WMT-2016 shared task on automatic post-editing and can demonstrate
that dual-attention models that incorporate all available data in the APE
scenario in a single model improve on the best shared task system and on all
other published results after the shared task. Dual-attention models that are
combined with hard attention remain competitive despite applying fewer changes
to the input.Comment: Accepted for presentation at IJCNLP 201
