TranslationCorrect: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance
This addresses the problem of disconnected workflows for translators and researchers in machine translation, though it appears incremental as it combines existing tools into a single environment.
The paper tackles the inefficiency of machine translation post-editing and research data collection by introducing TranslationCorrect, a unified framework that integrates MT generation, error prediction, and an intuitive interface, resulting in significantly improved translation efficiency and user satisfaction as confirmed by a user study.
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.