Machine Reading Comprehension (MRC) aims to understand a passage and answer a series of related questions. With the development of deep learning and the release of large-scale MRC datasets, many end-to-end MRC neural networks have achieved remarkable success. However, these models are fragile and lack of robustness when there are some imperceptible adversarial perturbations in the input. In this paper, we propose an MRC model which has two main components to improve the robustness. On the one hand, we enhance the representation of the model by leveraging hierarchical knowledge from external knowledge bases. On the other hand, we introduce an auxiliary unanswerability prediction module and perform supervised multi-task learning along with a span prediction task. Experimental results on benchmark datasets show that our model can achieve consistent improvement compared with other strong baselines.

机器阅读理解（MRC）旨在理解一段文章并回答一系列相关问题。随着深度学习的发展和大规模MRC数据集的发布，许多端到端MRC神经网络取得了显著成功。但是，当输入中存在一些难以察觉的对抗性扰动时，这些模型非常脆弱且缺乏鲁棒性。在本文中，我们提出了一个MRC模型，该模型具有两个主要成分以提高鲁棒性。一方面，我们通过利用来自外部知识库的分层知识来增强模型的表示。另一方面，我们引入了辅助不可回答性预测模块，并与跨度预测任务一起执行有监督的多任务学习。