Smarter edits? Post-editing with error highlights and translation suggestions
Fleur V.J. van Tellingen, Gautam Ranka, Dora \v{Z}ug\v{c}i\'c, Joyce van der Wal, Andrea Camasta, Livio Guerra, Alina Karakanta

TL;DR
This study evaluates the effectiveness of LLM-derived error highlights and correction suggestions in post-editing translations, finding improved user experience but no significant gains in productivity or quality.
Contribution
It provides empirical evidence on the usefulness of LLM-based post-editing aids compared to traditional methods.
Findings
APE highlights were better received than QE-derived highlights
Correction suggestions improved user experience
No significant productivity or quality improvements observed
Abstract
As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness remains limited. In this work, we explore the usefulness of LLM-derived error highlights and correction suggestions based on automatic post-editing (APE). We conduct a study where professional translators (En-Nl) post-edit translations using APE error highlights and correction suggestions and compare productivity, quality and user experience to regular PE and PE with QE-derived highlights. While no condition yielded productivity or quality gains compared to regular PE, APE highlights were better received than QE-derived highlights, and correction suggestions improved overall user experience.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
