Using Sentence‐Level Neural Network Models for Multiple‐Choice Reading Comprehension Tasks

Abstract

Comprehending unstructured text is a challenging task for machines because it involves understanding texts and answering questions. In this paper, we study the multiple‐choice task for reading comprehension based on MC Test datasets and Chinese reading comprehension datasets, among which Chinese reading comprehension datasets which are built by ourselves. Observing the above‐mentioned training sets, we find that “sentence comprehension” is more important than “word comprehension” in multiple‐choice task, and therefore we propose sentence‐level neural network models. Our model firstly uses LSTM network and a composition model to learn compositional vector representation for sentences and then trains a sentence‐level attention model for obtaining the sentence‐level attention between the sentence embedding in documents and the optional sentences embedding by dot product. Finally, a consensus attention is gained by merging individual attention with the merging function. Experimental results show that our model outperforms various state‐of‐the‐art baselines significantly for both the multiple‐choice reading comprehension datasets.