  There has been significant debate in the NLP community about whether or not
attention weights can be used as an explanation - a mechanism for interpreting
how important each input token is for a particular prediction. The validity of
"attention as explanation" has so far been evaluated by computing the rank
correlation between attention-based explanations and existing feature
attribution explanations using LSTM-based models. In our work, we (i) compare
the rank correlation between five more recent feature attribution methods and
two attention-based methods, on two types of NLP tasks, and (ii) extend this
analysis to also include transformer-based models. We find that attention-based
explanations do not correlate strongly with any recent feature attribution
methods, regardless of the model or task. Furthermore, we find that none of the
tested explanations correlate strongly with one another for the
transformer-based model, leading us to question the underlying assumption that
we should measure the validity of attention-based explanations based on how
well they correlate with existing feature attribution explanation methods.
After conducting experiments on five datasets using two different models, we
argue that the community should stop using rank correlation as an evaluation
metric for attention-based explanations. We suggest that researchers and
practitioners should instead test various explanation methods and employ a
human-in-the-loop process to determine if the explanations align with human
intuition for the particular use case at hand.
