  Knowledge Distillation (KD) is a predominant approach for BERT compression.
Previous KD-based methods focus on designing extra alignment losses for the
student model to mimic the behavior of the teacher model. These methods
transfer the knowledge in an indirect way. In this paper, we propose a novel
Weight-Inherited Distillation (WID), which directly transfers knowledge from
the teacher. WID does not require any additional alignment loss and trains a
compact student by inheriting the weights, showing a new perspective of
knowledge distillation. Specifically, we design the row compactors and column
compactors as mappings and then compress the weights via structural
re-parameterization. Experimental results on the GLUE and SQuAD benchmarks show
that WID outperforms previous state-of-the-art KD-based baselines. Further
analysis indicates that WID can also learn the attention patterns from the
teacher model without any alignment loss on attention distributions. The code
is available at https://github.com/wutaiqiang/WID-NAACL2024.
