TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance

Syed Mekael Wasti, Shou-Yi Hung, Christopher Collins, En-Shiun Annie Lee


Abstract
Machine translation (MT) post-editing and research data collection often rely on inefficient, disconnected workflows. We introduce **TranslationCorrect**, an integrated framework designed to streamline these tasks. **TranslationCorrect** combines MT generation using models like NLLB, automated error prediction using models like XCOMET or LLM APIs (providing detailed reasoning), and an intuitive post-editing interface within a single environment. Built with human-computer interaction (HCI) principles in mind to minimize cognitive load, as confirmed by a user study. For translators, it enables them to correct errors and batch translate efficiently. For researchers, **TranslationCorrect** exports high-quality span-based annotations in the Error Span Annotation (ESA) format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM). These outputs are compatible with state-of-the-art error detection models and suitable for training MT or post-editing systems. Our user study confirms that **TranslationCorrect** significantly improves translation efficiency and user satisfaction over traditional annotation methods.
Anthology ID:
2025.acl-demo.53
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Pushkar Mishra, Smaranda Muresan, Tao Yu
Venue:
ACL
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Publisher:
Association for Computational Linguistics
Note:
Pages:
551–562
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.53/
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Bibkey:
Cite (ACL):
Syed Mekael Wasti, Shou-Yi Hung, Christopher Collins, and En-Shiun Annie Lee. 2025. TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 551–562, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (Wasti et al., ACL 2025)
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https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.53.pdf
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 2025.acl-demo.53.copyright_agreement.pdf