Neural Machine Translation

Neural Machine Translation (NMT) aims to automatically translate text between languages using deep learning models, primarily focusing on improving translation accuracy and fluency. Current research emphasizes enhancing model robustness through techniques like contrastive learning to reduce repetition, leveraging translation memories and large language models (LLMs) for improved accuracy and efficiency, and addressing issues such as data scarcity in low-resource languages via data augmentation and transfer learning. These advancements have significant implications for cross-lingual communication, impacting fields ranging from international commerce to multilingual education and accessibility.