  Despite low latency, non-autoregressive machine translation (NAT) suffers
severe performance deterioration due to the naive independence assumption. This
assumption is further strengthened by cross-entropy loss, which encourages a
strict match between the hypothesis and the reference token by token. To
alleviate this issue, we propose multi-granularity optimization for NAT, which
collects model behaviors on translation segments of various granularities and
integrates feedback for backpropagation. Experiments on four WMT benchmarks
show that the proposed method significantly outperforms the baseline models
trained with cross-entropy loss, and achieves the best performance on WMT'16
En-Ro and highly competitive results on WMT'14 En-De for fully
non-autoregressive translation.
