ASSESSING NEURAL NETWORK PERFORMANCE IN AI-DRIVEN TRANSLATION: STRENGTHS AND CONSTRAINTS

Abstract

Neural networks have fundamentally transformed the field of artificial intelligence (AI)-based translation, introducing substantial improvements in accuracy, contextual understanding, and linguistic adaptability. This article examines the capabilities and limitations of neural machine translation (NMT) systems, structured according to the IMRaD format. Drawing on a review of recent literature, the study evaluates the architectural advances — including transformer models, attention mechanisms, and encoder-decoder frameworks — that underpin modern NMT performance. Findings indicate that while neural networks demonstrate superior accuracy and contextual sensitivity compared to traditional rule-based and statistical machine translation systems, they remain constrained by challenges related to polysemy, idiomatic expression, culturally specific language, and data quality. The implications of these limitations for future research and development in AI translation are discussed.