Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. However, existing knowledge distillation has side effects, such as propagating errors from the teacher to NAT students, which may limit further improvements of NAT models and are rarely discussed in existing research. In this paper, we introduce selective knowledge distillation by introducing an NAT evaluator to select NAT-friendly targets that are of high quality and easy to learn. In addition, we introduce a simple yet effective progressive distillation method to boost NAT performance. Experiment results on multiple WMT language directions and several representative NAT models show that our approach can realize a flexible trade-off between the quality and complexity of training data for NAT models, achieving strong performances. Further analysis shows that distilling only 5% of the raw translations can help an NAT outperform its counterpart trained on raw data by about 2.4 BLEU.

本文提出了一种选择性知识蒸馏技术，通过 NAT 评估器选择高质量且易于学习的 NAT 友好目标，并引入简单而有效的渐进式蒸馏方法来提高 NAT 性能，在多个 WMT 语言方向和多个代表性 NAT 模型上进行实验。结果表明，我们的方法可以实现训练数据的质量和复杂性之间的灵活权衡，取得了出色的性能。进一步的分析表明，只通过 5% 的原始翻译数据，就可以使 NAT 在 BLEU 值上比原始数据训练的对手高出约 2.4 BLEU。

非自回归神经机器翻译的选择性知识蒸馏