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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
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Outline Introduction Method Experiment Conclusion
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Introduction Motivation:
Increasing educational materials without sufficient related quiz questions Goal: To automate the question generation process
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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?
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Outline Introduction Method Experiment Conclusion
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Method Assumption: Answer sequence is a continuous segment within the corresponding context. Preprocessing: Making use of GloVe, d =300.
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Method Problem Formulation:
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Method Framework: Context reader Question generator
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Context Reader POS, NER are from Stanford NLP toolkit
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Pointer Network (Ptr-Net)
Seq2Seq’s defect: OOV when testing Scenario: Using input words.
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Question Generator
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Outline Introduction Method Experiment Conclusion
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Experiment Quantitative Eval. : On SQuAD.
Only using the sentence containing answer as inputs. Qualitative Eval. : On OpenStax.
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Quantitative Evaluation
@ The best!
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Scalability with training data
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Qualitative Evaluation
Similarity
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Results
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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.
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