Neural Machine Translation and Multilingual NLP: A Survey of Methods, Architectures, and Applications

Neural machine translation (NMT) has revolutionized the field of natural language processing by enabling high-quality automatic translation between languages using deep neural networks. This comprehensive survey examines the evolution of machine translation from statistical methods to modern neural approaches, with particular emphasis on the transformer architecture and its variants that have dominated the field since 2017. We systematically review the fundamental architectures including encoder-decoder models, attention mechanisms, and transformer-based systems, analyzing their theoretical foundations and practical implementations. The survey explores critical challenges in multilingual NLP including low-resource translation, zero-shot learning, cross-lingual transfer, and multimodal translation. We investigate recent advances in massively multilingual models, examining architectures that can translate between hundreds of language pairs within a single model. Furthermore, we discuss the emergence of large language models in translation tasks, analyzing their capabilities and limitations compared to dedicated translation systems. The paper also addresses practical considerations including evaluation metrics, data augmentation techniques, and deployment strategies for production systems. We provide insights into current research trends including document-level translation, simultaneous translation, and neural translation with external knowledge. By synthesizing research from over 100 papers, this survey offers both theoretical foundations and practical guidance for researchers and practitioners working in neural machine translation and multilingual natural language processing.