Cue word guided question generation with BERT model fine-tuned on natural question dataset

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dc.contributor.advisorSmith, Tony C.
dc.contributor.authorZhang, Zijing
dc.date.accessioned2020-11-17T23:21:21Z
dc.date.available2020-11-17T23:21:21Z
dc.date.issued2020
dc.date.updated2020-11-03T21:10:35Z
dc.description.abstractThis thesis aims to develop an efficient question generator for an automated tutoring system. Given a context passage with an answer, the question generator asks questions to help the reader learn new material. By utilizing the BERT model, this thesis experiments on generating type-specific questions with a cue word. This thesis also uses an RNN encoder-decoder architecture for question generation on SQuAD as a comparative baseline and fine-tune the BERT question generation model on Google's Natural Question dataset. Ultimately, I deliver a RESTful API by the end of this year-long master program.
dc.format.mimetypeapplication/pdf
dc.identifier.citationZhang, Z. (2020). Cue word guided question generation with BERT model fine-tuned on natural question dataset (Thesis, Master of Science (Research) (MSc(Research))). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/13965en
dc.identifier.urihttps://hdl.handle.net/10289/13965
dc.language.isoen
dc.publisherThe University of Waikato
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectDeep Learning
dc.subjectQuestion Generation
dc.subjectLanguage Model
dc.titleCue word guided question generation with BERT model fine-tuned on natural question dataset
dc.typeThesis
pubs.place-of-publicationHamilton, New Zealanden_NZ
thesis.degree.grantorThe University of Waikato
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (Research) (MSc(Research))
uow.author.twitter@zzj0402
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