Web: http://arxiv.org/abs/2205.01730

May 5, 2022, 1:11 a.m. | Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs'ka, Wenhao Liu, Caiming Xiong

cs.CL updates on arXiv.org arxiv.org

Question generation (QGen) models are often evaluated with standardized NLG
metrics that are based on n-gram overlap. In this paper, we measure whether
these metric improvements translate to gains in a practical setting, focusing
on the use case of helping teachers automate the generation of reading
comprehension quizzes. In our study, teachers building a quiz receive question
suggestions, which they can either accept or refuse with a reason. Even though
we find that recent progress in QGen leads to a …

arxiv design quiz

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