Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation. We train this agent using reinforcement learning with a novel reward function. Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.
REF propose a new operation PREDICT which predicts future source tokens to improve quality and minimize latency.
Prediction Improves Simultaneous Neural Machine Translation
EMNLP
Computer Science