Self-aware cycle curriculum learning for multiple-choice reading comprehension

Multiple-choice reading comprehension task has recently attracted significant interest. The task provides several options for each question and requires the machine to select one of them as the correct answer. Current approaches normally leverage a pre-training and then fine-tuning procedure that treats data equally, ignoring the difficulty of training examples. To solve this issue, curriculum learning (CL) has shown its effectiveness in improving the performance of models. However, previous methods have two problems with curriculum learning. First, most methods are rule-based, not flexible enough, and usually suitable for specific tasks, such as machine translation. Second, these methods arrange data from easy to hard or from hard to easy and overlook the fact that human beings usually learn from easy to difficult, and from difficult to easy when they make comprehension reading tasks. In this article, we propose a novel Self-Aware Cycle Curriculum Learning (SACCL) approach which can evaluate data difficulty from the model's perspective and train the model with cycle training strategy. The experiments show that the proposed approach achieves better performance on the C3 dataset than the baseline, which verifies the effectiveness of SACCL.