  Simultaneous machine translation (SiMT) starts to output translation while
reading the source sentence and needs a precise policy to decide when to output
the generated translation. Therefore, the policy determines the number of
source tokens read during the translation of each target token. However, it is
difficult to learn a precise translation policy to achieve good latency-quality
trade-offs, because there is no golden policy corresponding to parallel
sentences as explicit supervision. In this paper, we present a new method for
constructing the optimal policy online via binary search. By employing explicit
supervision, our approach enables the SiMT model to learn the optimal policy,
which can guide the model in completing the translation during inference.
Experiments on four translation tasks show that our method can exceed strong
baselines across all latency scenarios.
