Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs’ka, Wenhao Liu, Caiming Xiong
Abstract
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 significant increase in question acceptance rates, there is still large room for improvement, with the best model having only 68.4% of its questions accepted by the ten teachers who participated in our study. We then leverage the annotations we collected to analyze standard NLG metrics and find that model performance has reached projected upper-bounds, suggesting new automatic metrics are needed to guide QGen research forward.
BibTeX
@inproceedings{laban-etal-2022-quiz,
title = "Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation",
author = "Laban, Philippe and
Wu, Chien-Sheng and
Murakhovs{'}ka, Lidiya and
Liu, Wenhao and
Xiong, Caiming",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.9/",
doi = "10.18653/v1/2022.findings-naacl.9",
pages = "102--111"
}