  In simultaneous translation (SimulMT), the most widely used strategy is the
wait-k policy thanks to its simplicity and effectiveness in balancing
translation quality and latency. However, wait-k suffers from two major
limitations: (a) it is a fixed policy that can not adaptively adjust latency
given context, and (b) its training is much slower than full-sentence
translation. To alleviate these issues, we propose a novel and efficient
training scheme for adaptive SimulMT by augmenting the training corpus with
adaptive prefix-to-prefix pairs, while the training complexity remains the same
as that of training full-sentence translation models. Experiments on two
language pairs show that our method outperforms all strong baselines in terms
of translation quality and latency.
