  Human-annotated textual explanations are becoming increasingly important in
Explainable Natural Language Processing. Rationale extraction aims to provide
faithful (i.e., reflective of the behavior of the model) and plausible (i.e.,
convincing to humans) explanations by highlighting the inputs that had the
largest impact on the prediction without compromising the performance of the
task model. In recent works, the focus of training rationale extractors was
primarily on optimizing for plausibility using human highlights, while the task
model was trained on jointly optimizing for task predictive accuracy and
faithfulness. We propose REFER, a framework that employs a differentiable
rationale extractor that allows to back-propagate through the rationale
extraction process. We analyze the impact of using human highlights during
training by jointly training the task model and the rationale extractor. In our
experiments, REFER yields significantly better results in terms of
faithfulness, plausibility, and downstream task accuracy on both
in-distribution and out-of-distribution data. On both e-SNLI and CoS-E, our
best setting produces better results in terms of composite normalized relative
gain than the previous baselines by 11% and 3%, respectively.
