Abstract

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 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 rationale models show promise in improving robustness but struggle in certain scenarios---e.g., when the rationalizer is sensitive to position bias or lexical choices of the attack text. Further, leveraging human rationales as supervision does not always translate to better performance. Our study is a first step towards exploring the interplay between interpretability and robustness