OPTIMIZING QUALITY AND EFFICIENCY OF AI-GENERATED SCIENTIFIC AND TECHNICAL TRANSLATIONS THROUGH POST-EDITING

This article examines methods for optimizing the quality and efficiency of AI-generated scientific and technical translations with a particular focus on post-editing. As artificial intelligence increasingly supports professional translation workflows, post-editing has become a critical stage for ensuring accuracy, terminological consistency, and contextual adequacy in specialized texts. The study analyzes approaches to automating post-editing processes through the integration of intelligent tools designed to reduce manual effort and streamline editorial tasks. It further explores optimization technologies based on machine learning and neural network models, demonstrating their role in decreasing translation errors and minimizing post-editing time. These advancements are shown to significantly improve productivity when processing complex scientific and technical materials. In addition, the article discusses current trends in AI-assisted translation, including scalability and processing speed in large-scale projects. Post-editing is positioned as an essential quality assurance mechanism that enables AI systems to meet growing demands without compromising reliability. Ethical and contextual considerations are also addressed, emphasizing the importance of post-editing in preserving scientific meaning and terminological precision across languages. Overall, the findings highlight post-editing as a key factor in enhancing the effectiveness of AI-driven scientific and technical translation and provide practical insights for researchers and translation professionals.