Between Algorithm and Artistry: A Comparative Analysis of AI Translation Tools and Human Translators
DOI:
https://doi.org/10.69760/egjlle.2602026Keywords:
neural machine translation, human translation, AI translation tools, translation quality, cultural competence, post-editing, translator training, translation ethicsAbstract
The rapid advancement of neural machine translation (NMT) and large language model-based translation tools has intensified scholarly and professional debate about the comparative capabilities of artificial intelligence and human translators across a range of translation domains. This article provides a systematic comparative analysis of AI translation tools and human translators across five critical dimensions: linguistic accuracy and fluency, cultural competence and pragmatic appropriateness, handling of specialized and domain-specific discourse, treatment of ambiguity and contextual inference, and ethical and professional responsibility. Drawing on translation theory, cognitive linguistics, and recent empirical studies of AI translation quality, the analysis demonstrates that while contemporary AI translation systems have achieved remarkable fluency and near-human accuracy in literal, domain-stable, and high-resource language pair contexts, they remain systematically deficient in cultural mediation, pragmatic competence, creative adaptation, and the ethical judgment that responsible translation demands. The article argues that the most productive framing of the AI-human translator relationship is not competitive substitution but complementary integration: a model in which AI tools augment human translator productivity and consistency while human expertise addresses the communicative, cultural, and ethical dimensions that AI cannot reliably manage. Implications for translator training, the professional translation market, and the ethics of AI-mediated communication are discussed.
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