  Work on "learning with rationales" shows that humans providing explanations
to a machine learning system can improve the system's predictive accuracy.
However, this work has not been connected to work in "explainable AI" which
concerns machines explaining their reasoning to humans. In this work, we show
that learning with rationales can also improve the quality of the machine's
explanations as evaluated by human judges. Specifically, we present experiments
showing that, for CNN- based text classification, explanations generated using
"supervised attention" are judged superior to explanations generated using
normal unsupervised attention.
