Neural Machine Translation System

Neural Machine Translation (NMT) systems aim to automatically translate text between languages using deep learning models, primarily focusing on improving accuracy and efficiency across diverse language pairs, especially low-resource ones. Current research emphasizes techniques like back-translation and transfer learning to address data scarcity, explores novel architectures such as hybrid autoregressive/non-autoregressive models for faster inference, and investigates methods to mitigate biases and improve the quality of translations through techniques like quality estimation fusion and contextual guidance. These advancements have significant implications for cross-lingual communication, information access, and the development of more inclusive and robust AI systems.