Knowledge Based Machine Reading Comprehension

Understand

Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.

• However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability.
• To encourage the progress on knowledge-based reasoning in MRC, we present knowledge-based MRC in this paper, and build a new dataset consisting of 40,047 question-answer pairs.
• The annotation of this dataset is designed so that successfully answering the questions requires understanding and the knowledge involved in a document.