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

Abstract EN

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.