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.
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension
