Semi-automatic Construction of Bidirectional Dialogue Dataset for Dialogue-Based Reading Comprehension Tutoring System Using Generative AI

Author:

Choi Sung-Kwon,Huang Jin-XiaORCID,Kwon Oh-WoogORCID

Publisher

Springer Nature Switzerland

Reference12 articles.

1. Jeon, H.B., Chung, H., Kang, B.O., Lee, Y.K.: Survey of recent research in education based on artificial intelligence. Electron. Telecommun. Trendes 36(1), 71–80 (2021). (in Korean)

2. Huang, J.X., Lee, Y.H., Kwon, O.W.: DIRECT: toward dialogue-based reading comprehension tutoring. IEEE Access 11, 8978–8987 (2023)

3. Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: RACE: large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 785–794 (2017)

4. Yu, W., Jiang, Z., Dong, Y., Feng, J.: ReClor: a reading comprehension dataset requiring logical reasoning. In: Proceedings of 8th International Conference Learning Representation, pp. 1–26 (2020)

5. Reddy, S., Chen, D., Manning, C.D.: CoQA: a conversational question answering challenge. Trans. Assoc. Comput. Linguist. 7, 249–266 (2019)

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