Cue word guided question generation with BERT model fine-tuned on natural question dataset
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
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.
The University of Waikato
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- Masters Degree Theses