@inproceedings{saadi-etal-2023-learn,
    title = "Learn From One Specialized Sub-Teacher: One-to-One Mapping for Feature-Based Knowledge Distillation",
    author = "Saadi, Khouloud  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-emnlp.882/",
    doi = "10.18653/v1/2023.findings-emnlp.882",
    pages = "13235--13245",
    abstract = "Knowledge distillation is known as an effective technique for compressing 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 it focused. Our proposed 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. Furthermore, we propose a randomized variant of our approach, called Masked One-to-One Mapping. Rather than learning all the N sub-tasks simultaneously, we focus on learning a subset of these sub-tasks at each optimization step. This variant enables the student to digest the received flow of knowledge more effectively and yields superior results."
}