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.

本文提出了一种名为卷积空间注意力（CSA）模型的新方法，可以更好地处理机器阅读理解（MRC）与多项选择问题。该模型可以充分提取文章、问题和候选项之间的相互信息，形成丰富的表示，实验结果表明，该模型在RACE和SemEval-2018 Task11数据集上可以显著提高各种最先进系统的性能。

卷积空间注意力模型用于阅读理解多项选择题