RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformer

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.Comment: Accepted by AAAI2

Halpern iteration of Cesàro means for asymptotically nonexpansive mappings

ABSTRACT: Using a new proof technique which is independent of the approximation fixed point of T (limn→∞ xn − T xn = 0) and the convergence of the Browder type iteration path (zt = tu + (1 − t)T zt), the strong convergence of the Halpern iteration {xn} of Cesàro means for asymptotically nonexpansive self-mappings T , defined by xn+1 = αnu + (1 − αn)(n + 1) −1 n j=0 T j xn for n 0, is proved in a uniformly convex Banach space E with a uniformly Gâteaux differentiable norm whenever {αn} is a real sequence in (0, 1) satisfying the conditions limn→∞ bn/αn = 0 and limn→∞ αn = 0 and ∞ n=0 αn = ∞