  Neural rationale models are popular for interpretable predictions of NLP
tasks. In these, a selector extracts segments of the input text, called
rationales, and passes these segments to a classifier for prediction. Since the
rationale is the only information accessible to the classifier, it is plausibly
defined as the explanation. Is such a characterization unconditionally correct?
In this paper, we argue to the contrary, with both philosophical perspectives
and empirical evidence suggesting that rationale models are, perhaps, less
rational and interpretable than expected. We call for more rigorous and
comprehensive evaluations of these models to ensure desired properties of
interpretability are indeed achieved. The code can be found at
https://github.com/yimingz89/Neural-Rationale-Analysis.
