@inproceedings{wasti-etal-2025-translationcorrect,
title = "{TRANSLATIONCORRECT}: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance",
author = "Wasti, Syed Mekael and
Hung, Shou-Yi and
Collins, Christopher and
Lee, En-Shiun Annie",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.53/",
doi = "10.18653/v1/2025.acl-demo.53",
pages = "551--562",
ISBN = "979-8-89176-253-4",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance
%A Wasti, Syed Mekael
%A Hung, Shou-Yi
%A Collins, Christopher
%A Lee, En-Shiun Annie
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F wasti-etal-2025-translationcorrect
%X 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.
%R 10.18653/v1/2025.acl-demo.53
%U https://aclanthology.org/2025.acl-demo.53/
%U https://doi.org/10.18653/v1/2025.acl-demo.53
%P 551-562
Markdown (Informal)
[TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance](https://aclanthology.org/2025.acl-demo.53/) (Wasti et al., ACL 2025)
ACL