  Recent work in simultaneous machine translation is often trained with
conventional full sentence translation corpora, leading to either excessive
latency or necessity to anticipate as-yet-unarrived words, when dealing with a
language pair whose word orders significantly differ. This is unlike human
simultaneous interpreters who produce largely monotonic translations at the
expense of the grammaticality of a sentence being translated. In this paper, we
thus propose an algorithm to reorder and refine the target side of a full
sentence translation corpus, so that the words/phrases between the source and
target sentences are aligned largely monotonically, using word alignment and
non-autoregressive neural machine translation. We then train a widely used
wait-k simultaneous translation model on this reordered-and-refined corpus. The
proposed approach improves BLEU scores and resulting translations exhibit
enhanced monotonicity with source sentences.
