QG-Net: A Data-Driven Question Generation Model for Educational Content

Simple item page
dc.contributor.advisorBaraniuk, Richard
dc.creatorWang, Jack
dc.date.accessioned2020-04-28T17:01:59Z
dc.date.available2020-04-28T17:01:59Z
dc.date.created2020-05
dc.date.issued2020-04-23
dc.date.submittedMay 2020
dc.date.updated2020-04-28T17:01:59Z
dc.description.abstractThe ever growing amount of educational content renders it increasingly difficult to manually generate sufficient practice or quiz questions to accompany it. This thesis introduces QG-Net, a recurrent neural network-based model specifically designed for automatically generating quiz questions from educational content such as textbooks. QG-Net, when trained on a publicly available, general-purpose question/answer dataset and without further fine-tuning, is capable of generating high quality questions from textbooks, where the content is significantly different from the training data. Indeed, QG-Net outperforms state-of-the-art neural network-based and rules-based systems for question generation, both when evaluated using standard benchmark datasets and when using human evaluators. QG-Net also scales favorably to applications with large amounts of educational content, since its performance improves with the amount of training data.
dc.format.mimetypeapplication/pdf
dc.identifier.citationWang, Jack. "QG-Net: A Data-Driven Question Generation Model for Educational Content." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108451">https://hdl.handle.net/1911/108451</a>.
dc.identifier.urihttps://hdl.handle.net/1911/108451
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectautomatic question generation
dc.subjectpersonalized education
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectnatural language processing
dc.titleQG-Net: A Data-Driven Question Generation Model for Educational Content
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
WANG-DOCUMENT-2020.pdf
Size:
724.16 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.6 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Electronic Theses and Dissertations