Automatic post-editing (APE) aims to reduce manual post-editing efforts by
automatically correcting errors in machine-translated output. Due to the
limited amount of human-annotated training data, data scarcity is one of the
main challenges faced by all APE systems. To alleviate the lack of genuine
training data, most of the current APE systems employ data augmentation methods
to generate large-scale artificial corpora. In view of the importance of data
augmentation in APE, we separately study the impact of the construction method
of artificial corpora and artificial data domain on the performance of APE
models. Moreover, the difficulty of APE varies between different machine
translation (MT) systems. We study the outputs of the state-of-art APE model on
a difficult APE dataset to analyze the problems in existing APE systems.
Primarily, we find that 1) Artificial corpora with high-quality source text and
machine-translated text more effectively improve the performance of APE models;
2) In-domain artificial training data can better improve the performance of APE
models, while irrelevant out-of-domain data actually interfere with the model;
3) Existing APE model struggles with cases containing long source text or
high-quality machine-translated text; 4) The state-of-art APE model works well
on grammatical and semantic addition problems, but the output is prone to
entity and semantic omission errors.

该研究针对自动后编辑（APE）中数据稀缺的情况，通过数据增强的方式来生成大规模人工语料库构建人工数据域，以提高模型性能，报告表明高质量的人工语料库与生成的训练数据可以改善模型性能，同时在不同 MT 系统中 APE 的难度不同，模型在语法和语义添加问题上的表现良好，但容易出现实体和语义省略错误。