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Found 3 papers, 1 papers with code
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation
We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting.
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks at all.
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation
We then propose an alternative hypothesis under the lens of data augmentation and regularization.
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