  Attention-based methods have played important roles in model interpretations,
where the calculated attention weights are expected to highlight the critical
parts of inputs~(e.g., keywords in sentences). However, recent research found
that attention-as-importance interpretations often do not work as we expected.
For example, learned attention weights sometimes highlight less meaningful
tokens like "[SEP]", ",", and ".", and are frequently uncorrelated with other
feature importance indicators like gradient-based measures. A recent debate
over whether attention is an explanation or not has drawn considerable
interest. In this paper, we demonstrate that one root cause of this phenomenon
is the combinatorial shortcuts, which means that, in addition to the
highlighted parts, the attention weights themselves may carry extra information
that could be utilized by downstream models after attention layers. As a
result, the attention weights are no longer pure importance indicators. We
theoretically analyze combinatorial shortcuts, design one intuitive experiment
to show their existence, and propose two methods to mitigate this issue. We
conduct empirical studies on attention-based interpretation models. The results
show that the proposed methods can effectively improve the interpretability of
attention mechanisms.
