  Simultaneous translation, which translates sentences before they are
finished, is useful in many scenarios but is notoriously difficult due to
word-order differences. While the conventional seq-to-seq framework is only
suitable for full-sentence translation, we propose a novel prefix-to-prefix
framework for simultaneous translation that implicitly learns to anticipate in
a single translation model. Within this framework, we present a very simple yet
surprisingly effective wait-k policy trained to generate the target sentence
concurrently with the source sentence, but always k words behind. Experiments
show our strategy achieves low latency and reasonable quality (compared to
full-sentence translation) on 4 directions: zh<->en and de<->en.
