Option Attentive Capsule Network for Multi-choice Reading Comprehension

In this paper, we study the problem of multi-choice reading comprehension, which requires a machine to select the correct answer from a set of candidates based on the given passage and question. Most existing approaches focus on designing sophisticated attention to model the interactions of the sequence triplets (passage, question and candidate options), which aims to extract the answer clues from the passage. After this matching stage, a simple pooling operation is usually applied to aggregate the matching results to make final decisions. However, a bottom-up max or average pooling may loss essential information of the evidence clues and ignore the inter relationships of the sentences, especially dealing with complex questions when there are multiple evidence clues. To this end, we propose an option attentive capsule network with dynamic routing to overcome this issue. Instead of pooling, we introduce a capsule aggregating layer to dynamically fuse the information from multiple evidence clues and iteratively refine the matching representation. Furthermore, we design an option attention-based routing policy to focus more on each candidate option when clustering the features of low-level capsules. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on RACE dataset.