ConvBERT
ConvBERT
**ConvBERT** is a modification on the [BERT](https://paperswithcode.com/method/bert) architecture which uses a [span-based dynamic convolution](https://paperswithcode.com/method/span-based-dynamic-convolution) to replace self-attention heads to directly model local dependencies. Specifically a new [mixed attention module](https://paperswithcode.com/method/mixed-attention-block) replaces the [self-attention modules](https://paperswithcode.com/method/scaled) in BERT, which leverages the advantages of [convolution](https://paperswithcode.com/method/convolution) to better capture local dependency. Additionally, a new span-based dynamic convolution operation is used to utilize multiple input tokens to dynamically generate the convolution kernel. Lastly, ConvBERT also incorporates some new model designs including the bottleneck attention and grouped linear operator for the feed-forward module (reducing the number of parameters).
ConvBERT: Improving BERT with Span-based Dynamic Convolution
https://paperswithcode.com/paper/convbert-improving-bert-with-span-based
ConvBERT: Improving BERT with Span-based Dynamic Convolution