  To bridge the gap between Machine Reading Comprehension (MRC) models and
human beings, which is mainly reflected in the hunger for data and the
robustness to noise, in this paper, we explore how to integrate the neural
networks of MRC models with the general knowledge of human beings. On the one
hand, we propose a data enrichment method, which uses WordNet to extract
inter-word semantic connections as general knowledge from each given
passage-question pair. On the other hand, we propose an end-to-end MRC model
named as Knowledge Aided Reader (KAR), which explicitly uses the above
extracted general knowledge to assist its attention mechanisms. Based on the
data enrichment method, KAR is comparable in performance with the
state-of-the-art MRC models, and significantly more robust to noise than them.
When only a subset (20%-80%) of the training examples are available, KAR
outperforms the state-of-the-art MRC models by a large margin, and is still
reasonably robust to noise.
