@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://preview.aclanthology.org/cawl-year/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 \textbf{TranslationCorrect}, an integrated framework designed to streamline these tasks. \textbf{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, \textbf{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 \textbf{TranslationCorrect} significantly improves translation efficiency and user satisfaction over traditional annotation methods."
}