Blockwise Self-Attention for Long Document Understanding
Jiezhong Qiu, Hao Ma, Omer Levy, Wen-tau Yih, Sinong Wang, Jie Tang
Abstract
We present BlockBERT, a lightweight and efficient BERT model for better modeling longdistance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short-or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.