Year: 2017
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/d17-1219
Authors: Xinya Du, Claire Cardie
Institution: Cornell University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2757715585