  Existing techniques often attempt to make knowledge transfer from a powerful
machine translation (MT) to speech translation (ST) model with some elaborate
techniques, which often requires transcription as extra input during training.
However, transcriptions are not always available, and how to improve the ST
model performance without transcription, i.e., data efficiency, has rarely been
studied in the literature. In this paper, we propose Decoupled Non-parametric
Knowledge Distillation (DNKD) from data perspective to improve the data
efficiency. Our method follows the knowledge distillation paradigm. However,
instead of obtaining the teacher distribution from a sophisticated MT model, we
construct it from a non-parametric datastore via k-Nearest-Neighbor (kNN)
retrieval, which removes the dependence on transcription and MT model. Then we
decouple the classic knowledge distillation loss into target and non-target
distillation to enhance the effect of the knowledge among non-target logits,
which is the prominent "dark knowledge". Experiments on MuST-C corpus show
that, the proposed method can achieve consistent improvement over the strong
baseline without requiring any transcription.
