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.

本文针对 Integrated Gradients 在文本数据中由于词嵌入空间的固有离散性导致插值点不够具有代表性的问题，提出了基于离散化插值策略的 Discretized Integrated Gradients 方法，与 Integrated Gradients 方法相比，本方法计算出的梯度更符合实际情况，有效性得到了实验和人工评估的验证。