Existing approaches to neural machine translation are typically
autoregressive models. While these models attain state-of-the-art translation
quality, they are suffering from low parallelizability and thus slow at
decoding long sequences. In this paper, we propose a novel model for fast
sequence generation --- the semi-autoregressive Transformer (SAT). The SAT
keeps the autoregressive property in global but relieves in local and thus is
able to produce multiple successive words in parallel at each time step.
Experiments conducted on English-German and Chinese-English translation tasks
show that the SAT achieves a good balance between translation quality and
decoding speed. On WMT'14 English-German translation, the SAT achieves
5.58$\times$ speedup while maintains 88\% translation quality, significantly
better than the previous non-autoregressive methods. When produces two words at
each time step, the SAT is almost lossless (only 1\% degeneration in BLEU
score).

本论文提出一种用于快速序列生成的新型模型：半自回归 Transformer (SAT)，该模型在保持全局自回归属性的同时减轻了本地自回归属性，从而能够在每个时间步骤同时产生多个连续的词。在英德翻译和中英翻译任务上进行的实验证明，SAT 实现了翻译质量和解码速度之间的良好平衡。