Embedding Compression for Teacher-to-Student Knowledge Transfer
Yiwei Ding Alexander Lerch
Common knowledge distillation methods require the teacher model and the
student model to be trained on the same task. However, the usage of embeddings
as teachers has also been proposed for different source tasks and target tasks.
Prior work that uses embeddings as teachers ignores the fact that the teacher
embeddings are likely to contain irrelevant knowledge for the target task. To
address this problem, we propose to use an embedding compression module with a
trainable teacher transformation to obtain a compact teacher embedding. Results
show that adding the embedding compression module improves the classification
performance, especially for unsupervised teacher embeddings. Moreover, student
models trained with the guidance of embeddings show stronger generalizability.