  Automatic post-editing (APE) is an important remedy for reducing errors of
raw translated texts that are produced by machine translation (MT) systems or
software-aided translation. In this paper, we present the first attempt to
tackle the APE task for Vietnamese. Specifically, we construct the first
large-scale dataset of 5M Vietnamese translated and corrected sentence pairs.
We then apply strong neural MT models to handle the APE task, using our
constructed dataset. Experimental results from both automatic and human
evaluations show the effectiveness of the neural MT models in handling the
Vietnamese APE task.
