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Knowledge distillation is an approach to transfer information onrepresentations from a teacher to a student by reducing their difference. Achallenge of this approach is to reduce the flexibility of the student'srepresentations inducing inaccurate learning of the teacher's knowledge. Toresolve it in transferring, we investigate distillation of structures ofrepresentations specified to three types: intra-feature, local inter-feature,global inter-feature structures. To transfer them, we introduce featurestructure distillation methods based on the Centered Kernel Alignment, whichassigns a consistent value to similar features structures and reveals moreinformative relations. In particular, a memory-augmented transfer method withclustering is implemented for the global structures. The methods areempirically analyzed on the nine tasks for language understanding of the GLUEdataset with Bidirectional Encoder Representations from Transformers (BERT),which is a representative neural language model. In the results, the proposedmethods effectively transfer the three types of structures and improveperformance compared to state-of-the-art distillation methods. Indeed, the codefor the methods is available in https://github.com/maroo-sky/FSD.

Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring