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

      Zhang, Zijing
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      Zhang, 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/10289/13965
      Permanent Research Commons link: https://hdl.handle.net/10289/13965
      Abstract
      This 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.
      Date
      2020
      Type
      Thesis
      Degree Name
      Master of Science (Research) (MSc(Research))
      Supervisors
      Smith, Tony C.
      Publisher
      The University of Waikato
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      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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