@inproceedings{ang-etal-2023-socratic,
    title = "Socratic Question Generation: A Novel Dataset, Models, and Evaluation",
    author = "Ang, Beng Heng  and
      Gollapalli, Sujatha Das  and
      Ng, See-Kiong",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.eacl-main.12/",
    doi = "10.18653/v1/2023.eacl-main.12",
    pages = "147--165",
    abstract = "Socratic questioning is a form of reflective inquiry often employed in education to encourage critical thinking in students, and to elicit awareness of beliefs and perspectives in a subject during therapeutic counseling. Specific types of Socratic questions are employed for enabling reasoning and alternate views against the context of individual personal opinions on a topic. Socratic contexts are different from traditional question generation contexts where {\textquotedblleft}answer-seeking{\textquotedblright} questions are generated against a given formal passage on a topic, narrative stories or conversations. We present SocratiQ, the first large dataset of 110K (question, context) pairs for enabling studies on Socratic Question Generation (SoQG). We provide an in-depth study on the various types of Socratic questions and present models for generating Socratic questions against a given context through prompt tuning. Our automated and human evaluation results demonstrate that our SoQG models can produce realistic, type-sensitive, human-like Socratic questions enabling potential applications in counseling and coaching."
}