  Automatic post-editing (APE) systems aim to correct the systematic errors
made by machine translators. In this paper, we propose a neural APE system that
encodes the source (src) and machine translated (mt) sentences with two
separate encoders, but leverages a shared attention mechanism to better
understand how the two inputs contribute to the generation of the post-edited
(pe) sentences. Our empirical observations have showed that when the mt is
incorrect, the attention shifts weight toward tokens in the src sentence to
properly edit the incorrect translation. The model has been trained and
evaluated on the official data from the WMT16 and WMT17 APE IT domain
English-German shared tasks. Additionally, we have used the extra 500K
artificial data provided by the shared task. Our system has been able to
reproduce the accuracies of systems trained with the same data, while at the
same time providing better interpretability.
