NMT Model

Neural Machine Translation (NMT) aims to automatically translate text between languages using neural networks, primarily focusing on improving translation accuracy and efficiency. Current research emphasizes enhancing NMT models through techniques like incorporating large language models (LLMs) for improved fluency and handling of complex linguistic phenomena, optimizing model architectures (e.g., Transformers) for speed and resource efficiency (including mobile deployment), and employing methods such as k-Nearest-Neighbor (kNN) approaches for domain adaptation and knowledge distillation. These advancements have significant implications for both the scientific understanding of multilingual processing and practical applications, such as improving the accessibility and quality of translation services across various domains and languages.