  Simultaneous machine translation (SiMT) generates translation before reading
the entire source sentence and hence it has to trade off between translation
quality and latency. To fulfill the requirements of different translation
quality and latency in practical applications, the previous methods usually
need to train multiple SiMT models for different latency levels, resulting in
large computational costs. In this paper, we propose a universal SiMT model
with Mixture-of-Experts Wait-k Policy to achieve the best translation quality
under arbitrary latency with only one trained model. Specifically, our method
employs multi-head attention to accomplish the mixture of experts where each
head is treated as a wait-k expert with its own waiting words number, and given
a test latency and source inputs, the weights of the experts are accordingly
adjusted to produce the best translation. Experiments on three datasets show
that our method outperforms all the strong baselines under different latency,
including the state-of-the-art adaptive policy.
