Research Area:  Machine Learning

Abstract:

Automatic question generation is a promising tool for developing the learning systems of the future. Research in this area has mostly relied on having answers (key phrases) identified beforehand and given as a feature, which is not practical for real-world, scalable applications of question generation. We describe and implement an end-to-end neural question generation system that generates question and answer pairs given a context paragraph only. We accomplish this by first generating answer candidates (key phrases) from the paragraph context, and then generating questions using the key phrases. We evaluate our method of key phrase extraction by comparing our output over the same paragraphs with question-answer pairs generated by crowdworkers and by educational experts. Results demonstrate that our system is able to generate educationally meaningful question and answer pairs with only context paragraphs as input, significantly increasing the potential scalability of automatic question generation.

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Author(s) Name:  Angelica Willis , Glenn Davis , Sherry Ruan , Lakshmi Manoharan , James Landay , Emma Brunskill

Publisher name:  ACM

DOI:  10.1145/3330430.3333636

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3330430.3333636