In this work, we introduce a novel local autoregressive translation (LAT)
mechanism into non-autoregressive translation (NAT) models so as to capture
local dependencies among tar-get outputs. Specifically, for each target
decoding position, instead of only one token, we predict a short sequence of
tokens in an autoregressive way. We further design an efficient merging
algorithm to align and merge the out-put pieces into one final output sequence.
We integrate LAT into the conditional masked language model (CMLM;
Ghazvininejad et al.,2019) and similarly adopt iterative decoding. Empirical
results on five translation tasks show that compared with CMLM, our method
achieves comparable or better performance with fewer decoding iterations,
bringing a 2.5xspeedup. Further analysis indicates that our method reduces
repeated translations and performs better at longer sentences.

本研究介绍了一种新的局部自回归翻译机制，将其应用于非自回归翻译模型中，以捕捉目标输出的局部依赖关系。同时，设计出一种高效的合并算法来对齐和合并输出序列。我们在 5 个翻译任务上进行了实证结果，表明相比 CMLM，我们的方法在更少的译码迭代次数下取得了可比或更好的性能，加速了 2.5 倍。进一步的分析表明，我们的方法减少了重复翻译，并且在较长的句子上表现更好。