Key Challenges and Professional Tasks in the Field of Machine Translation Post-Editing

Denis Pshenichnikov

Citation: Denis Pshenichnikov, "Key Challenges and Professional Tasks in the Field of Machine Translation Post-Editing", Universal Library of Languages and Literatures, Volume 01, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article is devoted to the study of the main challenges and professional tasks in the field of machine translation post-editing. The relevance of this work lies in the fact that post-editing has become a dominant mode of production in the translation industry, requiring new professional competencies. The novelty is expressed in a comprehensive analysis of tasks faced by post-editors, from linguistic corrections to technical and analytical responsibilities in AI-driven workflows. Within the research, the evolution of the translator’s role is described, while difficulties such as cognitive load, adequacy errors, and stylistic inconsistencies are examined in detail. Special attention is given to how professional standards and training programs are adapting to the rise of MTPE. The study sets the objective of identifying the skill set necessary for effective post-editing and outlining the implications for translator education and industry practices. To achieve this goal, comparative analysis, content examination, and synthesis of scholarly and professional sources were applied. In conclusion, the article describes how MTPE is reshaping the profession and why human expertise remains indispensable. The study will be useful for researchers, practitioners, educators, and policymakers seeking to understand the transformation of translation work, its challenges, and opportunities in the digital age.


Keywords: Machine Translation, Post-Editing, Translation Quality, Neural MT, Professional Tasks.

Download doi https://doi.org/10.70315/uloap.ullli.2024.0101007