  A growing line of work has investigated the development of neural NLP models
that can produce rationales--subsets of input that can explain their model
predictions. In this paper, we ask whether such rationale models can also
provide robustness to adversarial attacks in addition to their interpretable
nature. Since these models need to first generate rationales ("rationalizer")
before making predictions ("predictor"), they have the potential to ignore
noise or adversarially added text by simply masking it out of the generated
rationale. To this end, we systematically generate various types of 'AddText'
attacks for both token and sentence-level rationalization tasks, and perform an
extensive empirical evaluation of state-of-the-art rationale models across five
different tasks. Our experiments reveal that the rationale models show the
promise to improve robustness, while they struggle in certain scenarios--when
the rationalizer is sensitive to positional bias or lexical choices of attack
text. Further, leveraging human rationale as supervision does not always
translate to better performance. Our study is a first step towards exploring
the interplay between interpretability and robustness in the
rationalize-then-predict framework.
