A Self-Supervised Automatic Post-Editing Data Generation Tool
This tool reduces human effort in data building for APE, facilitating data-centric research in understudied language pairs.
The paper tackles the problem of labor-intensive data creation for automatic post-editing by developing a self-supervised tool that generates personalized APE data from parallel corpora for multiple language pairs, enabling research in previously unexplored languages.
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, we develop a self-supervised data generation tool, deployable as a web application, that minimizes human supervision and constructs personalized APE data from a parallel corpus for several language pairs with English as the target language. Data-centric APE research can be conducted using this tool, involving many language pairs that have not been studied thus far owing to the lack of suitable data.