Automatic Generation of Socratic Subquestions for Teaching Math Word Problems

Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, Mrinmaya Sachan


Abstract
Socratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactically sound questions is challenging, requiring understanding of the reasoning process involved in the problem. We hypothesize that such questioning strategy can not only enhance the human performance, but also assist the math word problem (MWP) solvers.In this work, we explore the ability of large language models (LMs) in generating sequential questions for guiding math word problem-solving. We propose various guided question generation schemes based on input conditioning and reinforcement learning.On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions and improve the overall performance of a math word problem solver. We conduct a preliminary user study to examine the potential value of such question generation models in the education domain. Results suggest that the difficulty level of problems plays an important role in determining whether questioning improves or hinders human performance. We discuss the future of using such questioning strategies in education.
Anthology ID:
2022.emnlp-main.277
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4136–4149
Language:
URL:
https://aclanthology.org/2022.emnlp-main.277
DOI:
10.18653/v1/2022.emnlp-main.277
Bibkey:
Cite (ACL):
Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, Tanmay Sinha, Manu Kapur, and Mrinmaya Sachan. 2022. Automatic Generation of Socratic Subquestions for Teaching Math Word Problems. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4136–4149, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (Shridhar et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2022.emnlp-main.277.pdf