  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.
