In addition, to encourage ReDR to generate meaningful and interesting questions, ideally, one may employ humans to provide feedback, but as widely acknowledged, involving humans in the loop for training models can be very costly.
in this paper, we leverage a popular and effective reading comprehension (or QA) model (Chen et al., 2017) to predict the answer to a generated question and use its answer quality (which can be seen as a proxy for real human feedback) as rewards to fine-tune our model based on a reinforcement learning mechanism (Williams, 1992).