Translation Process

Translation research focuses on improving the accuracy, efficiency, and fluency of translating text and speech across languages. Current efforts concentrate on refining machine translation (MT) models, employing techniques like sparse regression, multi-agent collaboration, and prompt engineering to enhance translation quality, particularly for long-form texts and specialized terminology. These advancements leverage large language models (LLMs) and neural networks, addressing challenges such as idiom translation, code-switching, and maintaining stability in simultaneous speech translation. The resulting improvements have significant implications for fields requiring cross-lingual communication, including international business, scientific collaboration, and access to information.