Enhanced question understanding with dynamic memory networks for textual question answering

•Introducing global and hierarchical salient features of inputs.•Adopting a modified network to extract hierarchical salient features of a question.•Finding a method to utilize these features to construct multiple feature sets.

Memory networks show promising context understanding and reasoning capabilities in Textual Question Answering (Textual QA). We improve the previous dynamic memory networks to do Textual QA by processing inputs to simultaneously extract global and hierarchical salient features. We then use them to construct multiple feature sets at each reasoning step. Experiments were conducted on a public Textual Question Answering dataset (Facebook bAbI dataset) in two ways: with and without supervision from labels of supporting facts. Compared to previous works such as Dynamic Memory Networks, our models show better accuracy and stability.