Before revision:  In this paper, we focus on "teaching" machines on reading comprehensionwith ( a small number of ) natural language explanations . We propose a data augmentation framework that exploits the compositional nature of explanations to rapidly create pseudo-labeled data for training downstream MRC models.
After revision:  In this paper, we focus on "teaching" machines on reading comprehensionwith ( a small number of semi-structured explanations that explicitly inform machines why answer spans are correct. We extract structured variables and rules from explanations and compose neural module teachers that annotate instances for training downstream MRC models.