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.

本论文提出了一种基于 Mixture-of-Experts Wait-k 机制的通用即时翻译模型，采用多头注意力实现专家混合，每个专家根据其自己的等待词数被认为是 wait-k 专家，并根据测试延迟和源输入调整专家权重以产生最佳翻译，可以在任意延迟下实现最佳翻译质量，实验结果表明，该方法优于现有方法。