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Semi-supervised learning that leverages synthetic training data has beenwidely adopted in the field of Automatic post-editing (APE) to overcome thelack of human-annotated training data. In that context, data-synthesis methodsto create high-quality synthetic data have also received much attention.Considering that APE takes machine-translation outputs containing translationerrors as input, we propose a noising-based data-synthesis method that uses amask language model to create noisy texts through substituting masked tokenswith erroneous tokens, yet following the error-quantity statistics appearing ingenuine APE data. In addition, we propose corpus interleaving, which is tocombine two separate synthetic data by taking only advantageous samples, tofurther enhance the quality of the synthetic data created with our noisingmethod. Experimental results reveal that using the synthetic data created withour approach results in significant improvements in APE performance upon usingother synthetic data created with different existing data-synthesis methods.

Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens