  By computing the rank correlation between attention weights and
feature-additive explanation methods, previous analyses either invalidate or
support the role of attention-based explanations as a faithful and plausible
measure of salience. To investigate whether this approach is appropriate, we
compare LIME, Integrated Gradients, DeepLIFT, Grad-SHAP, Deep-SHAP, and
attention-based explanations, applied to two neural architectures trained on
single- and pair-sequence language tasks. In most cases, we find that none of
our chosen methods agree. Based on our empirical observations and theoretical
objections, we conclude that rank correlation does not measure the quality of
feature-additive methods. Practitioners should instead use the numerous and
rigorous diagnostic methods proposed by the community.
