Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods. (arXiv:2211.08369v1 [cs.CL])

A popular approach to unveiling the black box of neural NLP models is to
leverage saliency methods, which assign scalar importance scores to each input
component. A common practice for evaluating whether an interpretability method
is \textit{faithful} and \textit{plausible} has been to use
evaluation-by-agreement -- multiple methods agreeing on an explanation
increases its credibility. However, recent work has found that even saliency
methods have weak rank correlations and advocated for the use of alternative
diagnostic methods. In our work, we …

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