  The computational benefits of iterative non-autoregressive transformers
decrease as the number of decoding steps increases. As a remedy, we introduce
Distill Multiple Steps (DiMS), a simple yet effective distillation technique to
decrease the number of required steps to reach a certain translation quality.
The distilled model enjoys the computational benefits of early iterations while
preserving the enhancements from several iterative steps. DiMS relies on two
models namely student and teacher. The student is optimized to predict the
output of the teacher after multiple decoding steps while the teacher follows
the student via a slow-moving average. The moving average keeps the teacher's
knowledge updated and enhances the quality of the labels provided by the
teacher. During inference, the student is used for translation and no
additional computation is added. We verify the effectiveness of DiMS on various
models obtaining 7.8 and 12.9 BLEU points improvements in single-step
translation accuracy on distilled and raw versions of WMT'14 De-En.
