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

Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy