ALGORITHM VS. INTUITION: A COMPARATIVE STUDY OF NEURAL AND HUMAN TRANSLATION STRATEGIES

Abstract

This article examines the differences between human translation and neural machine translation (NMT), focusing on how each handle meaning and context. Human translators use cultural knowledge, context, and pragmatics to produce translations that reflect the intent and nuances of the original text. NMT systems rely on mathematical models, word embeddings, and attention mechanisms to generate translations quickly and fluently. However, machines often struggle with ambiguity, idiomatic expressions, stylistic choices, and ethical concerns such as bias. The article also discusses hybrid approaches, where humans review and adjust machine-generated translations to ensure clarity, consistency, and cultural appropriateness. The analysis shows that while machines are useful for routine tasks, human translators remain crucial for precise, nuanced, and context-sensitive work.