Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text.Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy document.In this paper, we propose a novel deep neural network architecture to handle a long-range dependency in RC tasks.In detail, our method has two novel aspects: (1) an advanced memory-augmented architecture and (2) an expanded gated recurrent unit with dense connections that mitigate potential information distortion occurring in the memory.Our proposed architecture is widely applicable to other models.We have performed extensive experiments with well-known benchmark datasets such as TriviaQA, QUASAR-T, and SQuAD.The experimental results demonstrate that the proposed method outperforms existing methods, especially for lengthy documents.

In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks.Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document.We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer.To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset 'Nar-rativeQA'.The proposed architecture outperforms state-of-the-art results (Tay et al., 2018) by 12.62% (ROUGE-L) relative improvement.