  Transformers-based models, such as BERT, have been one of the most successful
deep learning models for NLP. Unfortunately, one of their core limitations is
the quadratic dependency (mainly in terms of memory) on the sequence length due
to their full attention mechanism. To remedy this, we propose, BigBird, a
sparse attention mechanism that reduces this quadratic dependency to linear. We
show that BigBird is a universal approximator of sequence functions and is
Turing complete, thereby preserving these properties of the quadratic, full
attention model. Along the way, our theoretical analysis reveals some of the
benefits of having $O(1)$ global tokens (such as CLS), that attend to the
entire sequence as part of the sparse attention mechanism. The proposed sparse
attention can handle sequences of length up to 8x of what was previously
possible using similar hardware. As a consequence of the capability to handle
longer context, BigBird drastically improves performance on various NLP tasks
such as question answering and summarization. We also propose novel
applications to genomics data.
