Application and Innovative Practices of Generative AI in Professional Translation

Abstract

This study investigates the role of generative AI in professional translation, focusing on its applications, challenges, and future prospects. It begins by comparing generative AI with Neural Machine Translation (NMT), highlighting generative AI's advantages in semantic comprehension, contextual coherence, and specialized terminology handling-benefits particularly evident in legal, medical, and technological translation. The study then outlines three core applications of generative AI: optimized workflows for domain-specific translation, AI-integrated collaborative translation models encompassing pre-translation, in-translation, and post-translation support, and real-time cross-linguistic communication tools. It further elucidates the human-AI collaboration mechanism, wherein AI manages standardized tasks such as basic translation and terminology calibration, while human translators focus on high-value work such as cultural adaptation, collectively achieving translation quality comparable to purely human output. The study also identifies key challenges, including limited long-tail terminology, cultural adaptation biases, data privacy concerns, and non-standardized workflows, and proposes corresponding solutions, such as domain-specific fine-tuning, industry guidelines, and encryption protocols. Finally, it forecasts future trends in translation: the expansion of multimodal translation, the emergence of cloud-based real-time collaborative ecosystems, and the increasing demand for translators with composite competencies combining domain expertise, AI proficiency, and cultural literacy.