In most of neural machine translation distillation or stealing scenarios, the
goal is to preserve the performance of the target model (teacher). The
highest-scoring hypothesis of the teacher model is commonly used to train a new
model (student). If reference translations are also available, then better
hypotheses (with respect to the references) can be upsampled and poor
hypotheses either removed or undersampled.
This paper explores the importance sampling method landscape (pruning,
hypothesis upsampling and undersampling, deduplication and their combination)
with English to Czech and English to German MT models using standard MT
evaluation metrics. We show that careful upsampling and combination with the
original data leads to better performance when compared to training only on the
original or synthesized data or their direct combination.

本文探究了利用重要性抽样法进行神经机器翻译中的知识蒸馏，包括剪枝、假设上采样和下采样、去重和它们的组合，并使用标准的翻译质量评估方法对英德和英捷翻译模型进行训练与测试，结果显示，仔细选择合适的数据进行上采样并与原始数据组合，可获得更好的性能提升。