Also, unlike prior work on improving interpretability (Erion et al., 2019;Plumb et al., 2019), it does not require pre-defined important attributions or pre-collected explanations.
we propose variational word masks (VMASK) that are inserted into a neural text classifier, after the word embedding layer, and trained jointly with the model.