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

Machine translation (MT) post-editing and research data collection often rely on inefficient, disconnected workflows. We introduce TRANS-LATIONCORRECT, an integrated framework designed to streamline these tasks. TRANSLA-TIONCORRECT 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, TRANSLATIONCORRECT makes it easier for annotators to perform annotations, as confirmed by a user study using NASA Task Load Indices. 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.