THE HUMAN-MACHINE INTERFACE IN TRANSLATION: RE-EVALUATING PROFESSIONAL COMPETENCIES IN THE AGE OF NEURAL MACHINE TRANSLATION AND POST-EDITING

Abstract

The advent of Neural Machine Translation (NMT) has fundamentally reshaped the landscape of professional translation, necessitating a critical re-evaluation of established practices, quality metrics, and the very definition of translator competence. This article explores the transformative impact of NMT on the translation industry, focusing specifically on the burgeoning paradigm of Post-Editing Machine Translation (PEMT). It examines how the human translator's role is evolving from primary text producer to skilled editor and quality controller, analyzing the new competencies required and the ethical implications arising from this human-machine interface. Through a discussion of current research and industry trends, this paper argues that effective integration of NMT and PEMT requires a recalibration of translator education, a nuanced understanding of quality, and a robust framework for professional development in an increasingly automated environment.