Back to Search Start Over

A Self-Supervised Automatic Post-Editing Data Generation Tool

Authors :
Moon, Hyeonseok
Park, Chanjun
Eo, Sugyeong
Seo, Jaehyung
Lee, SeungJun
Lim, Heuiseok
Publication Year :
2021

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.<br />Comment: Accepted for DataPerf workshop at ICML 2022

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2111.12284
Document Type :
Working Paper