  We take a deep look into the behavior of self-attention heads in the
transformer architecture. In light of recent work discouraging the use of
attention distributions for explaining a model's behavior, we show that
attention distributions can nevertheless provide insights into the local
behavior of attention heads. This way, we propose a distinction between local
patterns revealed by attention and global patterns that refer back to the
input, and analyze BERT from both angles. We use gradient attribution to
analyze how the output of an attention attention head depends on the input
tokens, effectively extending the local attention-based analysis to account for
the mixing of information throughout the transformer layers. We find that there
is a significant discrepancy between attention and attribution distributions,
caused by the mixing of context inside the model. We quantify this discrepancy
and observe that interestingly, there are some patterns that persist across all
layers despite the mixing.
