  Although existing machine reading comprehension models are making rapid
progress on many datasets, they are far from robust. In this paper, we propose
an understanding-oriented machine reading comprehension model to address three
kinds of robustness issues, which are over sensitivity, over stability and
generalization. Specifically, we first use a natural language inference module
to help the model understand the accurate semantic meanings of input questions
so as to address the issues of over sensitivity and over stability. Then in the
machine reading comprehension module, we propose a memory-guided multi-head
attention method that can further well understand the semantic meanings of
input questions and passages. Third, we propose a multilanguage learning
mechanism to address the issue of generalization. Finally, these modules are
integrated with a multi-task learning based method. We evaluate our model on
three benchmark datasets that are designed to measure models robustness,
including DuReader (robust) and two SQuAD-related datasets. Extensive
experiments show that our model can well address the mentioned three kinds of
robustness issues. And it achieves much better results than the compared
state-of-the-art models on all these datasets under different evaluation
metrics, even under some extreme and unfair evaluations. The source code of our
work is available at: https://github.com/neukg/RobustMRC.
