  Transformer models have achieved superior performance in various natural
language processing tasks. However, the quadratic computational cost of the
attention mechanism limits its practicality for long sequences. There are
existing attention variants that improve the computational efficiency, but they
have limited ability to effectively compute global information. In parallel to
Transformer models, state space models (SSMs) are tailored for long sequences,
but they are not flexible enough to capture complicated local information. We
propose SPADE, short for $\underline{\textbf{S}}$tate
s$\underline{\textbf{P}}$ace
$\underline{\textbf{A}}$ugmente$\underline{\textbf{D}}$
Transform$\underline{\textbf{E}}$r. Specifically, we augment a SSM into the
bottom layer of SPADE, and we employ efficient local attention methods for the
other layers. The SSM augments global information, which complements the lack
of long-range dependency issue in local attention methods. Experimental results
on the Long Range Arena benchmark and language modeling tasks demonstrate the
effectiveness of the proposed method. To further demonstrate the scalability of
SPADE, we pre-train large encoder-decoder models and present fine-tuning
results on natural language understanding and natural language generation
tasks.
