  Sparse Transformer has recently attracted a lot of attention since the
ability for reducing the quadratic dependency on the sequence length. We argue
that two factors, information bottleneck sensitivity and inconsistency between
different attention topologies, could affect the performance of the Sparse
Transformer. This paper proposes a well-designed model named ERNIE-Sparse. It
consists of two distinctive parts: (i) Hierarchical Sparse Transformer (HST) to
sequentially unify local and global information. (ii) Self-Attention
Regularization (SAR) method, a novel regularization designed to minimize the
distance for transformers with different attention topologies. To evaluate the
effectiveness of ERNIE-Sparse, we perform extensive evaluations. Firstly, we
perform experiments on a multi-modal long sequence modeling task benchmark,
Long Range Arena (LRA). Experimental results demonstrate that ERNIE-Sparse
significantly outperforms a variety of strong baseline methods including the
dense attention and other efficient sparse attention methods and achieves
improvements by 2.77% (57.78% vs. 55.01%). Secondly, to further show the
effectiveness of our method, we pretrain ERNIE-Sparse and verified it on 3 text
classification and 2 QA downstream tasks, achieve improvements on
classification benchmark by 0.83% (92.46% vs. 91.63%), on QA benchmark by 3.24%
(74.67% vs. 71.43%). Experimental results continue to demonstrate its superior
performance.
