  Sparse attention has been claimed to increase model interpretability under
the assumption that it highlights influential inputs. Yet the attention
distribution is typically over representations internal to the model rather
than the inputs themselves, suggesting this assumption may not have merit. We
build on the recent work exploring the interpretability of attention; we design
a set of experiments to help us understand how sparsity affects our ability to
use attention as an explainability tool. On three text classification tasks, we
verify that only a weak relationship between inputs and co-indexed intermediate
representations exists -- under sparse attention and otherwise. Further, we do
not find any plausible mappings from sparse attention distributions to a sparse
set of influential inputs through other avenues. Rather, we observe in this
setting that inducing sparsity may make it less plausible that attention can be
used as a tool for understanding model behavior.
