  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.
