Smarnet: Teaching Machines to Read and Comprehend Like Human

Understand

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.