DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation

TL;DR

DiMS is a distillation technique that reduces the number of decoding steps in iterative non-autoregressive transformers, maintaining translation quality while improving computational efficiency.

Contribution

Introduces DiMS, a distillation method that enables single-step translation with iterative transformer benefits, without extra inference cost.

Findings

Achieves 7.8 BLEU improvement on distilled models

Achieves 12.9 BLEU improvement on raw models

Enhances translation accuracy with fewer decoding steps

Abstract

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.