When Machines Translate: Artificial Intelligence, Human Judgment, and the Future of Translation
DOI:
https://doi.org/10.69760/portuni.26060002Keywords:
Artificial intelligence, machine translation, neural machine translation, large language models, human–machine collaboration, translation studies, cognitive partnership, cultural competence, translator trainingAbstract
The rapid advancement of artificial intelligence has profoundly reshaped the landscape of translation, shifting it from a craft practiced exclusively by human specialists to a hybrid domain where neural machine translation systems, large language models, and AI-assisted tools play increasingly central roles. This article critically examines the capabilities and limitations of contemporary AI translation systems, with particular attention to the Transformer architecture, neural machine translation, and large language models. It argues that while AI has achieved remarkable fluency at the level of sentence-level grammar and lexical accuracy, it continues to fall short in areas requiring cultural sensitivity, pragmatic awareness, and contextual judgment — precisely the domains where human translators remain indispensable. Drawing on research in translation studies, computational linguistics, cognitive science, and language education, the article develops a framework for understanding the complementary relationship between human and machine translation. It proposes a model of ‘cognitive partnership’ in which AI tools augment rather than replace human translators, and discusses the implications of this model for translator training, professional ethics, and the future of the translation industry. The article also addresses the emotional and cultural dimensions of language that resist algorithmic capture, and reflects on what the rise of AI translation reveals about the nature of human language itself.
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