  Machine Comprehension (MC) is a challenging task in Natural Language
Processing field, which aims to guide the machine to comprehend a passage and
answer the given question. Many existing approaches on MC task are suffering
the inefficiency in some bottlenecks, such as insufficient lexical
understanding, complex question-passage interaction, incorrect answer
extraction and so on. In this paper, we address these problems from the
viewpoint of how humans deal with reading tests in a scientific way.
Specifically, we first propose a novel lexical gating mechanism to dynamically
combine the words and characters representations. We then guide the machines to
read in an interactive way with attention mechanism and memory network. Finally
we add a checking layer to refine the answer for insurance. The extensive
experiments on two popular datasets SQuAD and TriviaQA show that our method
exceeds considerable performance than most state-of-the-art solutions at the
time of submission.
