A Self-Supervised Automatic Post-Editing Data Generation Tool

TL;DR

This paper introduces a self-supervised, web-deployable tool that automatically generates data for automatic post-editing, reducing human effort and enabling research across multiple language pairs.

Contribution

It presents a novel self-supervised data generation tool for APE that minimizes human supervision and supports multiple language pairs, expanding research possibilities.

Findings

Enables large-scale APE data creation with minimal human effort

Supports multiple language pairs including low-resource languages

Facilitates data-centric APE research and development

Abstract

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.