Reward Models To Improve Question Specificity In Deep Learning Question Generation - ON-123

Preferred Disciplines and Level: Computer Science, Computational Linguistics, Computer Engineering. Post-Doc or Existing Ph.D. Student.
Company: Crater Labs Inc.
Project Length: 8-12 months (2 units)
Desired start date: As soon as possible
Location: Toronto, Ontario
No. of Positions: 1
Preferences: We would prefer to work with a university in the Greater Toronto Area or Southern Ontario. Language: English, Bilingual          

About the Company: 

We are a Toronto-based studio specializing in the use of computer vision, predictive analytics and natural language processing to build intelligence into business applications.

Project Description:

Recent research has indicated that a recurrent neural network can be trained to generate questions from documents. We seek to examine how common-sense reasoning methods can be employed in conjunction with these question generation models to create abstract and well posed questions.

Research Objectives:​

  • Implement a common-sense reasoning method within the RNN model as proposed in Machine Comprehension by Text-to-Text Neural Question Generation, to determine the degree to which such an approach can generate abstract and well-posed question.
  • Apply this model to semi-structured business documents to determine if such a model can generate abstract and well-posed documents from provided source materials.

Methodology:

  • Implement and train RNN based on reference contained in  Machine Comprehension by Text-to-Text Neural Question Generation
  • Examine efficacy of model on provided sample business documents
  • Examine approaches to implement commonsense reasoning that may impact the ability of the RNN to generate abstract/interesting questions

Expertise and Skills Needed:

  • Familiarity with basic natural language processing concepts
  • Familiarity with deep learning libraries such as TensorFlow or Caffe
  • Knowledge of Python, matplotlib, numpy, pandas and Jupyter Notebooks

For more info or to apply to this applied research position, please

  1. Check your eligibility and find more information about open projects.

  2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform.

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Program: