  Simultaneous machine translation, which aims at a real-time translation, is
useful in many live scenarios but very challenging due to the trade-off between
accuracy and latency. To achieve the balance for both, the model needs to wait
for appropriate streaming text (READ policy) and then generates its translation
(WRITE policy). However, WRITE policies of previous work either are specific to
the method itself due to the end-to-end training or suffer from the input
mismatch between training and decoding for the non-end-to-end training.
Therefore, it is essential to learn a generic and better WRITE policy for
simultaneous machine translation. Inspired by strategies utilized by human
interpreters and "wait" policies, we propose a novel adaptive prefix-to-prefix
training policy called LEAPT, which allows our machine translation model to
learn how to translate source sentence prefixes and make use of the future
context. Experiments show that our proposed methods greatly outperform
competitive baselines and achieve promising results.
