Simultaneous Machine Translation

Simultaneous machine translation (SiMT) aims to generate target language translations in real-time as source language input is received, balancing translation quality with minimal latency. Current research focuses on developing adaptive read/write policies, often integrated with large language models (LLMs) or employing novel architectures like non-autoregressive transformers, to optimize this trade-off. These advancements are improving the accuracy and efficiency of SiMT, with implications for real-time communication technologies such as simultaneous interpretation and speech translation. The field is also actively exploring improved evaluation metrics that better capture the nuances of real-time translation performance.