EVALUATING THE CAPABILITIES AND LIMITATIONS OF NEURAL NETWORKS IN AI TRANSLATION

Abstract

This article examines the capabilities and limitations of neural networks in artificial intelligence (AI) translation. Neural machine translation (NMT) systems, particularly those based on deep learning architectures such as sequence-to-sequence models, transformers, and attention mechanisms, have significantly improved translation accuracy, fluency, and contextual understanding. These technologies enable machines to process large volumes of linguistic data and generate translations that are more natural and contextually appropriate than traditional rule-based and statistical methods. Despite these advancements, neural networks still face several challenges, including difficulties in interpreting idiomatic expressions, cultural references, sarcasm, and linguistic nuances. Their performance is also highly dependent on the quality, diversity, and representativeness of training data. Biased or insufficient datasets may lead to translation errors and reduced effectiveness, especially for underrepresented languages and dialects. The article highlights the importance of improving contextual awareness, enhancing data quality, and integrating human linguistic expertise with AI technologies to develop more reliable and culturally sensitive translation systems. Future research should focus on addressing these limitations to further improve the accuracy and effectiveness of AI-driven translation.