1. A Data-Driven Question Generation Model for Educational Content
QG-Net
A Data-Driven Question Generation Model for Educational Content
Speaker: Yin-Hsiang Liao
Advisor: Jia-Lin Koh
Date: Feb 25 19
Source: 2018

2. Outline
Introduction
Method
Experiment
Conclusion

3. Introduction Motivation:
Increasing educational materials without sufficient related quiz questions
Goal:
To automate the question generation process

4. Introduction Challenge: Producing fluent and relevant questions.
Limited training data in educational applications.
Example:
Context: Of course, doing a test cross in humans is unethical and impractical.
Question: What is unethical in humans?

5. Outline
Introduction
Method
Experiment
Conclusion

6. Method
Assumption:
Answer sequence is a continuous segment within the corresponding context.
Preprocessing:
Making use of GloVe, d =300.

7. Method
Problem Formulation:

8. Method
Framework:
Context reader
Question generator

9. Context Reader
POS, NER are from Stanford NLP toolkit

10. Pointer Network (Ptr-Net)
Seq2Seq’s defect:
OOV when testing
Scenario:
Using input words.

12. Question Generator

13. Outline
Introduction
Method
Experiment
Conclusion

14. Experiment Quantitative Eval. : On SQuAD.
Only using the sentence containing answer as inputs.
Qualitative Eval. :
On OpenStax.

15. Quantitative Evaluation@
The best!

16. Scalability with training data

17. Qualitative Evaluation
Similarity

18. Results

20. Conclusion Limitation: Contribution: the QG-net system.
No guaranteed to always generated good questions.
The need of human experts to review.
Contribution: the QG-net system.