Abstract
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-ofthe-art MRC models by a large margin, and is
still reasonably robust to noise.