  Simultaneous machine translation (SiMT) starts its translation before reading
the whole source sentence and employs either fixed or adaptive policy to
generate the target sentence. Compared to the fixed policy, the adaptive policy
achieves better latency-quality tradeoffs by adopting a flexible translation
policy. If the policy can evaluate rationality before taking action, the
probability of incorrect actions will also decrease. However, previous methods
lack evaluation of actions before taking them. In this paper, we propose a
method of performing the adaptive policy via integrating post-evaluation into
the fixed policy. Specifically, whenever a candidate token is generated, our
model will evaluate the rationality of the next action by measuring the change
in the source content. Our model will then take different actions based on the
evaluation results. Experiments on three translation tasks show that our method
can exceed strong baselines under all latency.
