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Conference Paper
Semi-Automatic Construction of Bidirectional Dialogue Dataset for Dialogue-based Reading Comprehension Tutoring System using Generative AI
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- Authors
- Sung-Kwon Choi, Jin-Xia Huang, Oh-Woog Kwon
- Issue Date
- 2024-06
- Citation
- International Conference on Intelligent Tutoring Systems (ITS) 2024 (LNCS 14799), pp.305-313
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- The goal of this paper is to semi-automatically construct a bidirectional reading comprehension dialogue dataset that enables bidirectional dialogue or debate on reading passages within a dialogue-based reading comprehension tutoring system. To achieve this goal, we developed a process for semi-automatically constructing bidirectional reading comprehension dialogue dataset. Using this process, ten English experts were able to construct 2,951 datasets, with an average difficulty level of 8.24 (high school level) and an average dialogue turn count of 9.75 per passage.
- KSP Keywords
- Difficulty level, Reading comprehension, Semi-automatic construction, automatically construct, high school level, tutoring system