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Semi-supervised learning that leverages synthetic data for training has beenwidely adopted for developing automatic post-editing (APE) models due to thelack of training data. With this aim, we focus on data-synthesis methods tocreate high-quality synthetic data. Given that APE takes as input amachine-translation result that might include errors, we present adata-synthesis method by which the resulting synthetic data mimic thetranslation errors found in actual data. We introduce a noising-baseddata-synthesis method by adapting the masked language model approach,generating a noisy text from a clean text by infilling masked tokens witherroneous tokens. Moreover, we propose selective corpus interleaving thatcombines two separate synthetic datasets by taking only the advantageoussamples to enhance the quality of the synthetic data further. Experimentalresults show that using the synthetic data created by our approach results insignificantly better APE performance than other synthetic data created byexisting methods.

Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms