  Non-autoregressive translation models (NAT) have achieved impressive
inference speedup. A potential issue of the existing NAT algorithms, however,
is that the decoding is conducted in parallel, without directly considering
previous context. In this paper, we propose an imitation learning framework for
non-autoregressive machine translation, which still enjoys the fast translation
speed but gives comparable translation performance compared to its
auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and
WMT16 datasets. Our proposed model achieves a significant speedup over the
autoregressive models, while keeping the translation quality comparable to the
autoregressive models. By sampling sentence length in parallel at inference
time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and
30.68 BLEU on IWSLT16 En$\rightarrow$De.
