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With the growth of computing power neural machine translation (NMT) modelsalso grow accordingly and become better. However, they also become harder todeploy on edge devices due to memory constraints. To cope with this problem, acommon practice is to distill knowledge from a large and accurately-trainedteacher network (T) into a compact student network (S). Although knowledgedistillation (KD) is useful in most cases, our study shows that existing KDtechniques might not be suitable enough for deep NMT engines, so we propose anovel alternative. In our model, besides matching T and S predictions we have acombinatorial mechanism to inject layer-level supervision from T to S. In thispaper, we target low-resource settings and evaluate our translation engines forPortuguese--English, Turkish--English, and English--German directions. Studentstrained using our technique have 50% fewer parameters and can still delivercomparable results to those of 12-layer teachers.

Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers