# A Self-Supervised Automatic Post-Editing Data Generation Tool

arXiv: https://arxiv.org/abs/2111.12284 · HTML: https://mlsanity.com/paper/2111.12284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing](https://mlsanity.com/paper/1803.07274/) (2018)
- [How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus](https://mlsanity.com/paper/2111.00191/) (2021)

## Abstract (arXiv)

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.