  As a prominent attribution-based explanation algorithm, Integrated Gradients
(IG) is widely adopted due to its desirable explanation axioms and the ease of
gradient computation. It measures feature importance by averaging the model's
output gradient interpolated along a straight-line path in the input data
space. However, such straight-line interpolated points are not representative
of text data due to the inherent discreteness of the word embedding space. This
questions the faithfulness of the gradients computed at the interpolated points
and consequently, the quality of the generated explanations. Here we propose
Discretized Integrated Gradients (DIG), which allows effective attribution
along non-linear interpolation paths. We develop two interpolation strategies
for the discrete word embedding space that generates interpolation points that
lie close to actual words in the embedding space, yielding more faithful
gradient computation. We demonstrate the effectiveness of DIG over IG through
experimental and human evaluations on multiple sentiment classification
datasets. We provide the source code of DIG to encourage reproducible research.
