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 “answer-seeking” 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.- Anthology ID:
- 2023.eacl-main.12
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–165
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.12
- DOI:
- 10.18653/v1/2023.eacl-main.12
- Cite (ACL):
- Beng Heng Ang, Sujatha Das Gollapalli, and See-Kiong Ng. 2023. Socratic Question Generation: A Novel Dataset, Models, and Evaluation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 147–165, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Socratic Question Generation: A Novel Dataset, Models, and Evaluation (Ang et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.eacl-main.12.pdf