Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory

Masaki Uto, Yuto Tomikawa, Ayaka Suzuki


Abstract
Question generation (QG) for reading comprehension, a technology for automatically generating questions related to given reading passages, has been used in various applications, including in education. Recently, QG methods based on deep neural networks have succeeded in generating fluent questions that are pertinent to given reading passages. One example of how QG can be applied in education is a reading tutor that automatically offers reading comprehension questions related to various reading materials. In such an application, QG methods should provide questions with difficulty levels appropriate for each learner’s reading ability in order to improve learning efficiency. Several difficulty-controllable QG methods have been proposed for doing so. However, conventional methods focus only on generating questions and cannot generate answers to them. Furthermore, they ignore the relation between question difficulty and learner ability, making it hard to determine an appropriate difficulty for each learner. To resolve these problems, we propose a new method for generating question–answer pairs that considers their difficulty, estimated using item response theory. The proposed difficulty-controllable generation is realized by extending two pre-trained transformer models: BERT and GPT-2.
Anthology ID:
2023.bea-1.10
Volume:
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–129
Language:
URL:
https://aclanthology.org/2023.bea-1.10
DOI:
10.18653/v1/2023.bea-1.10
Bibkey:
Cite (ACL):
Masaki Uto, Yuto Tomikawa, and Ayaka Suzuki. 2023. Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 119–129, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory (Uto et al., BEA 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.bea-1.10.pdf