@inproceedings{perkoff-etal-2023-comparing,
    title = "Comparing Neural Question Generation Architectures for Reading Comprehension",
    author = "Perkoff, E. Margaret  and
      Bhattacharyya, Abhidip  and
      Cai, Jon  and
      Cao, Jie",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.47",
    doi = "10.18653/v1/2023.bea-1.47",
    pages = "556--566",
    abstract = "In recent decades, there has been a significant push to leverage technology to aid both teachers and students in the classroom. Language processing advancements have been harnessed to provide better tutoring services, automated feedback to teachers, improved peer-to-peer feedback mechanisms, and measures of student comprehension for reading. Automated question generation systems have the potential to significantly reduce teachers{'} workload in the latter. In this paper, we compare three differ- ent neural architectures for question generation across two types of reading material: narratives and textbooks. For each architecture, we explore the benefits of including question attributes in the input representation. Our models show that a T5 architecture has the best overall performance, with a RougeL score of 0.536 on a narrative corpus and 0.316 on a textbook corpus. We break down the results by attribute and discover that the attribute can improve the quality of some types of generated questions, including Action and Character, but this is not true for all models.",
}

In recent decades, there has been a significant push to leverage technology to aid both teachers and students in the classroom. Language processing advancements have been harnessed to provide better tutoring services, automated feedback to teachers, improved peer-to-peer feedback mechanisms, and measures of student comprehension for reading. Automated question generation systems have the potential to significantly reduce teachers{'} workload in the latter. In this paper, we compare three differ- ent neural architectures for question generation across two types of reading material: narratives and textbooks. For each architecture, we explore the benefits of including question attributes in the input representation. Our models show that a T5 architecture has the best overall performance, with a RougeL score of 0.536 on a narrative corpus and 0.316 on a textbook corpus. We break down the results by attribute and discover that the attribute can improve the quality of some types of generated questions, including Action and Character, but this is not true for all models.
Perkoff, E. Margaret Bhattacharyya, Abhidip Cai, Jon Cao, Jie
Comparing Neural Question Generation Architectures for Reading Comprehension