Rationale-Inspired Natural Language Explanations with Commonsense

Models that generate extractive rationales (ERs) (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an ER provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehensive description of the decision-making process behind a prediction. However, current models that generate the best ERs or NLEs often fall behind the state-of-the-art (SOTA) in terms of task performance. In this work, we bridge this gap by introducing RExC, a self-rationalizing framework that grounds its predictions and two complementary types of explanations (NLEs and ERs) in background knowledge. RExC improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations while existing models usually provide only one, and (iii) beating by a large margin the previous SOTA in terms of quality of explanations. Furthermore, a perturbation analysis in RExC shows a high degree of association between explanations and predictions, a necessary property of faithful explanations.