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

Telling BERT's full story: from Local Attention to Global Aggregation