  Neural network models that can handle inputs of variable lengths are
powerful, but often hard to interpret. The lack of transparency hinders their
adoption in many domains. Explanation techniques are essential for improving
transparency. However, existing model-agnostic general explanation techniques
do not consider the variable lengths of input data points, which limits their
effectiveness. To address this limitation, we propose ReX, a general framework
for adapting various explanation techniques to models that process
variable-length inputs, expanding explanation coverage to data points of
different lengths. Our approach adds temporal information to the explanations
generated by existing techniques without altering their core algorithms. We
instantiate our approach on two popular explanation techniques: LIME and
Anchors. To evaluate the effectiveness of ReX, we apply our approach to three
models in two different tasks. Our evaluation results demonstrate that our
approach significantly improves the fidelity and understandability of
explanations.
