  Pre-trained language models like BERT and its variants have recently achieved
impressive performance in various natural language understanding tasks.
However, BERT heavily relies on the global self-attention block and thus
suffers large memory footprint and computation cost. Although all its attention
heads query on the whole input sequence for generating the attention map from a
global perspective, we observe some heads only need to learn local
dependencies, which means the existence of computation redundancy. We therefore
propose a novel span-based dynamic convolution to replace these self-attention
heads to directly model local dependencies. The novel convolution heads,
together with the rest self-attention heads, form a new mixed attention block
that is more efficient at both global and local context learning. We equip BERT
with this mixed attention design and build a ConvBERT model. Experiments have
shown that ConvBERT significantly outperforms BERT and its variants in various
downstream tasks, with lower training cost and fewer model parameters.
Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than
ELECTRAbase, while using less than 1/4 training cost. Code and pre-trained
models will be released.
