Towards Semi-Supervised Learning of Automatic Post-Editing: Data-Synthesis by Infilling Mask with Erroneous Tokens. (arXiv:2204.03896v1 [cs.CL])

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 …

arxiv data data-synthesis learning semi-supervised semi-supervised learning supervised learning tokens