Advancements in Machine Translation: Techniques, Challenges, and Future Prospects

Abstract

Machine translation (MT) has experienced rapid advancements in recent years, largely driven by breakthroughs in neural networks and deep learning. From rule-based methods to statistical approaches and now to neural machine translation (NMT), the field has undergone substantial transformation. This article reviews the key techniques, developments, and challenges in MT, emphasizing the transition from traditional methods to neural-based models. It also explores the future prospects of MT, particularly its integration with artificial intelligence, multilingualism, and real-time applications. Key challenges, including handling idiomatic expressions, low-resource languages, and domain-specific translation, are discussed, along with potential solutions and areas for improvement.