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Knowledge−Grounded Self−Rationalization via Extractive and Natural Language Explanations

Bodhisattwa Prasad Majumder‚ Oana−Maria Camburu‚ Thomas Lukasiewicz and Julian McAuley


An increasing number of works focus on building models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions. While an extractive rationale 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 extractive rationales 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 extractive rationales) in background knowledge. Our framework 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 type, 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.

Book Title
Proceedings of the 39th International Conference on Machine Learning‚ ICML 2022‚ Baltimore‚ Maryland‚ USA‚ 17−23 July 2022
Proceedings of Machine Learning Research