  This paper describes the submission of the RoyalFlush neural machine
translation system for the WMT 2022 translation efficiency task. Unlike the
commonly used autoregressive translation system, we adopted a two-stage
translation paradigm called Hybrid Regression Translation (HRT) to combine the
advantages of autoregressive and non-autoregressive translation. Specifically,
HRT first autoregressively generates a discontinuous sequence (e.g., make a
prediction every $k$ tokens, $k>1$) and then fills in all previously skipped
tokens at once in a non-autoregressive manner. Thus, we can easily trade off
the translation quality and speed by adjusting $k$. In addition, by integrating
other modeling techniques (e.g., sequence-level knowledge distillation and
deep-encoder-shallow-decoder layer allocation strategy) and a mass of
engineering efforts, HRT improves 80\% inference speed and achieves equivalent
translation performance with the same-capacity AT counterpart. Our fastest
system reaches 6k+ words/second on the GPU latency setting, estimated to be
about 3.1x faster than the last year's winner.
