  Machine Reading Comprehension (MRC) with multiple-choice questions requires
the machine to read given passage and select the correct answer among several
candidates. In this paper, we propose a novel approach called Convolutional
Spatial Attention (CSA) model which can better handle the MRC with
multiple-choice questions. The proposed model could fully extract the mutual
information among the passage, question, and the candidates, to form the
enriched representations. Furthermore, to merge various attention results, we
propose to use convolutional operation to dynamically summarize the attention
values within the different size of regions. Experimental results show that the
proposed model could give substantial improvements over various
state-of-the-art systems on both RACE and SemEval-2018 Task11 datasets.
