Learn from One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation

Abstract

Knowledge Distillation is known as an effective technique to compress over-parameterized language models. In this work, we propose to break down the global feature distillation task into N local sub-tasks. In this new framework, we consider each neuron in the last hidden layer of the teacher network as a specialized sub-teacher. We also consider each neuron in the last hidden layer of the student network as a focused sub-student. We make each focused sub-student learn from one corresponding specialized sub-teacher and ignore the others. This will facilitate the task for the sub-student and keep him focused. This method is novel and can be combined with other distillation techniques. Empirical results show that our proposed approach outperforms the state-of-the-art methods by maintaining higher performance on most benchmark datasets.