A Translation Competence-Based Approach to Machine Translation Post-Editing Training
Abstract
Introduction: This study examined the role of machine translation post-editing within the language service industry, where it had become the dominant workflow alongside continued challenges in translator training systems. Grounded in the PACTE translation competence model, the study addressed limitations in pragmatic accuracy and cultural adequacy in machine translation output by designing a three-stage, competence-oriented post-editing training program. Methodology: A conceptual and theory-driven methodology was adopted, without empirical data collection. A modular curriculum was developed, integrating theoretical instruction, case-based analysis, and project-based practice. In addition, an assessment framework combining formative and summative evaluation was proposed to support learner development and performance measurement. Results: The findings indicated that the structured three-stage model could enhance learners’ ability to detect and correct pragmatic errors, while strategic sub-competence played a central coordinating role in skill development. Project-based learning was also found to foster critical thinking and improve alignment with market demands. The proposed 60/40 balance between formative and summative assessment further supported both learning processes and outcome quality. Conclusions: The study concluded that the translation competence framework provided a viable basis for post-editing training design and curriculum reform.