  Recently, a new training oaxe loss has proven effective to ameliorate the
effect of multimodality for non-autoregressive translation (NAT), which removes
the penalty of word order errors in the standard cross-entropy loss. Starting
from the intuition that reordering generally occurs between phrases, we extend
oaxe by only allowing reordering between ngram phrases and still requiring a
strict match of word order within the phrases. Extensive experiments on NAT
benchmarks across language pairs and data scales demonstrate the effectiveness
and universality of our approach. %Further analyses show that the proposed
ngram-oaxe alleviates the multimodality problem with a better modeling of
phrase translation. Further analyses show that ngram-oaxe indeed improves the
translation of ngram phrases, and produces more fluent translation with a
better modeling of sentence structure.
