It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
This addresses the need for teachers to create educational questions efficiently, though it is incremental as it adapts existing QA techniques to a new domain.
The paper tackles the problem of automatically generating question-answer pairs to test children's comprehension of storybooks, resulting in a model that outperforms state-of-the-art baselines on a new dataset of 278 storybooks with 10,580 QA pairs.
Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer generation (QAG) system for this education scenario: given a story book at the kindergarten to eighth-grade level as input, our system can automatically generate QA pairs that are capable of testing a variety of dimensions of a student's comprehension skills. Our proposed QAG model architecture is demonstrated using a new expert-annotated FairytaleQA dataset, which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and human evaluations show that our model outperforms state-of-the-art QAG baseline systems. On top of our QAG system, we also start to build an interactive story-telling application for the future real-world deployment in this educational scenario.