  Non-autoregressive translation (NAT) model achieves a much faster inference
speed than the autoregressive translation (AT) model because it can
simultaneously predict all tokens during inference. However, its translation
quality suffers from degradation compared to AT. And existing NAT methods only
focus on improving the NAT model's performance but do not fully utilize it. In
this paper, we propose a simple but effective method called "Candidate Soups,"
which can obtain high-quality translations while maintaining the inference
speed of NAT models. Unlike previous approaches that pick the individual result
and discard the remainders, Candidate Soups (CDS) can fully use the valuable
information in the different candidate translations through model uncertainty.
Extensive experiments on two benchmarks (WMT'14 EN-DE and WMT'16 EN-RO)
demonstrate the effectiveness and generality of our proposed method, which can
significantly improve the translation quality of various base models. More
notably, our best variant outperforms the AT model on three translation tasks
with 7.6 times speedup.
