  Saliency maps have shown to be both useful and misleading for explaining
model predictions especially in the context of images. In this paper, we
perform sanity checks for text modality and show that the conclusions made for
image do not directly transfer to text. We also analyze the effects of the
input multiplier in certain saliency maps using similarity scores,
max-sensitivity and infidelity evaluation metrics. Our observations reveal that
the input multiplier carries input's structural patterns in explanation maps,
thus leading to similar results regardless of the choice of model parameters.
We also show that the smoothness of a Neural Network (NN) function can affect
the quality of saliency-based explanations. Our investigations reveal that
replacing ReLUs with Softplus and MaxPool with smoother variants such as
LogSumExp (LSE) can lead to explanations that are more reliable based on the
infidelity evaluation metric.
