  Semi-supervised learning that leverages synthetic data for training has been
widely adopted for developing automatic post-editing (APE) models due to the
lack of training data. With this aim, we focus on data-synthesis methods to
create high-quality synthetic data. Given that APE takes as input a
machine-translation result that might include errors, we present a
data-synthesis method by which the resulting synthetic data mimic the
translation errors found in actual data. We introduce a noising-based
data-synthesis method by adapting the masked language model approach,
generating a noisy text from a clean text by infilling masked tokens with
erroneous tokens. Moreover, we propose selective corpus interleaving that
combines two separate synthetic datasets by taking only the advantageous
samples to enhance the quality of the synthetic data further. Experimental
results show that using the synthetic data created by our approach results in
significantly better APE performance than other synthetic data created by
existing methods.
