Identifying Where to Focus in Reading Comprehension for Neural Question Generation
Xinya Du, Claire Cardie · 2017
A first step in the task of automatically generating questions for testing reading comprehension is to identify questionworthy sentences, i.e. sentences in a text passage that humans find it worthwhile to ask questions about.We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored.The approach is fully data-driven -with no sophisticated NLP pipelines or any hand-crafted rules/features -and compares favorably to a number of baselines when evaluated on the SQuAD data set.When incorporated into an existing neural question generation system, the resulting end-to-end system achieves stateof-the-art performance for paragraph-level question generation for reading comprehension.