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

通过使用掩码语言模型来创建包含错误标记的文本的噪声数据合成方法，以及利用受益样本的语料库交错的方法，可以提高半监督学习在自动编辑中的性能。