  Recent approaches to the Automatic Post-Editing (APE) research have shown
that better results are obtained by multi-source models, which jointly encode
both source (src) and machine translation output (mt) to produce post-edited
sentence (pe). Along this trend, we present a new multi-source APE model based
on the Transformer. To construct effective joint representations, our model
internally learns to incorporate src context into mt representation. With this
approach, we achieve a significant improvement over baseline systems, as well
as the state-of-the-art multi-source APE model. Moreover, to demonstrate the
capability of our model to incorporate src context, we show that the word
alignment of the unknown MT system is successfully captured in our encoding
results.
