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 improvements of up to 7 BLEU points on distilled and 12 BLEU
points on raw WMT datasets for single-step translation. We release our code at
this https URL.

本文提出了一种叫做 Distill Multiple Steps 的简单而有效的蒸馏技术，它可以通过使用学生模型和教师模型，使得输出的质量得到提高，且能够减少到达特定翻译质量所需的步骤数。