  Non-autoregressive neural machine translation (NAT) models are proposed to
accelerate the inference process while maintaining relatively high performance.
However, existing NAT models are difficult to achieve the desired
efficiency-quality trade-off. For one thing, fully NAT models with efficient
inference perform inferior to their autoregressive counterparts. For another,
iterative NAT models can, though, achieve comparable performance while
diminishing the advantage of speed. In this paper, we propose RenewNAT, a
flexible framework with high efficiency and effectiveness, to incorporate the
merits of fully and iterative NAT models. RenewNAT first generates the
potential translation results and then renews them in a single pass. It can
achieve significant performance improvements at the same expense as traditional
NAT models (without introducing additional model parameters and decoding
latency). Experimental results on various translation benchmarks (e.g.,
\textbf{4} WMT) show that our framework consistently improves the performance
of strong fully NAT methods (e.g., GLAT and DSLP) without additional speed
overhead.
