NEURAL MACHINE TRANSLATION: A CRITICAL ANALYSIS OF CURRENT CAPABILITIES AND INHERENT LIMITATIONS (Web of Discoveries: Journal of Analysis and Inventions, 2026)
This article critically examines the current capabilities and inherent limitations of neural machine translation (NMT). It highlights how advancements in deep learning, particularly transformer architectures and attention mechanisms, have significantly improved translation accuracy, fluency, and contextual understanding. Despite these achievements, NMT systems still face notable challenges, including difficulties with contextual ambiguity, idiomatic expressions, cultural nuances, and low-resource languages. Furthermore, the effectiveness of these systems is highly dependent on the quality and diversity of training data, which can introduce bias and limit generalization. The paper emphasizes the need for continued research into improving contextual awareness, data representation, and hybrid approaches that combine artificial intelligence with human linguistic expertise. Addressing these limitations is essential for developing more reliable, inclusive, and culturally sensitive machine translation systems.