@inproceedings{rabin-etal-2023-covering,
    title = "Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment",
    author = "Rabin, Roni  and
      Djerbetian, Alexandre  and
      Engelberg, Roee  and
      Hackmon, Lidan  and
      Elidan, Gal  and
      Tsarfaty, Reut  and
      Globerson, Amir",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.20",
    doi = "10.18653/v1/2023.acl-short.20",
    pages = "215--227",
    abstract = "Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance.",
}

Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance.
Rabin, Roni Djerbetian, Alex re Engelberg, Roee Hackmon, Lidan Elidan, Gal Tsarfaty, Reut Globerson, Amir
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment