Abstract
In this chapter, question generation techniques were briefly reviewed, and a question-paragraph mapping task was identified. Then, we described our method to solve the mapping task and the preliminary results. In specific, given a set of questions generated from a semantic network and a list of paragraphs, the mapping task was to map the related paragraphs to each question. To conduct a first step evaluation, two undergraduate students were recruited to make connections between 53 paragraphs and 54 questions. The two students first submitted their works separately and discuss to resolve their conflicts together. The mutual agreement work was treated as the gold standards. By comparing the machine-generated mapping to the human-generated mapping, it showed that the machine-generated mapping (F measure: 0.68) performed as good as human-generated ones (F measure: 0.67/0.66). It implied that the mapping technique could be potentially used to give students the recommendation for further learning materials.
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References
Ali, H., Chali, Y., & Hasan, S. A. (2010). Automation of question generation from sentences. Paper presented at the Proceedings of QG2010: The Third Workshop on Question Generation.
Anderson, R. C., & Biddle, W. B. (1975). On asking people questions about what they are reading. Psychology of Learning and Motivation, 9, 89–132.
Baral, C., Vo, N. H., & Liang, S. (2012). Answering why and how questions with respect to a frame-based knowledge base: A preliminary report. Paper presented at the ICLP (Technical Communications).
Cao, X., Cong, G., Cui, B., Jensen, C. S., & Zhang, C. (2009). The use of categorization information in language models for question retrieval. Paper presented at the Proceedings of the 18th ACM conference on Information and knowledge management.
Curto, S., Mendes, A. C., & Coheur, L. (2012). Question generation based on Lexico-Syntactic patterns learned from the Web. Dialogue & Discourse, 3(2), 147–175.
Du, X., Shao, J., & Cardie, C. (2017). Learning to ask: Neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106.
Fader, A., Zettlemoyer, L. S., & Etzioni, O. (2013). Paraphrase-driven learning for open question answering. Paper presented at the ACL (1).
Fellbaum, C. (1998). WordNet: Wiley Online Library.
Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., et al. (2010). Building Watson: An overview of the DeepQA project. AI Magazine, 31(3), 59–79.
Gao, J., & Nie, J.-Y. (2012). Towards concept-based translation models using search logs for query expansion. Paper presented at the Proceedings of the 21st ACM international conference on Information and knowledge management.
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, 36(2), 180–192.
Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254.
Olney, A., Graesser, A. C., & Person, N. (2012). Question generation from concept maps. Dialogue and Discourse, 3(2), 75–99.
Puente, C., Sobrino, A., & Olivas, J. A. (2009). Extraction of conditional and causal sentences from queries to provide a flexible answer. Paper presented at the Proceedings of the 8th International Conference on Flexible Query Answering Systems.
Robertson, S. E., Walker, S., & Hancock-Beaulieu, M. (2000). Experimentation as a way of life: okapi at trec. Information Processing & Management, 36(1), 95–108.
Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research, 66(2), 181–221.
Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–5223.
Xue, X., Jeon, J., & Croft, W. B. (2008). Retrieval models for question and answer archives. Paper presented at the Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval.
Zhang, L., & VanLehn, K. (2016). How do machine-generated questions compare to human-generated questions? Research and Practice in Technology Enhanced Learning, 11(1), 7.
Zhang, L., & VanLehn, K. (2017). Adaptively selecting biology questions generated from a semantic network. Interactive Learning Environments, 25(7), 828–846.
Zhang, L., & VanLehn, K. (2019). Evaluation of auto-generated distractors in multiple choice questions from a semantic network. Interactive Learning Environments, 1–9. https://doi.org/10.1080/10494820.2019.1619586
Zhang, X., Lapata, M., Wei, F., & Zhou, M. (2018). Neural latent extractive document summarization. arXiv preprint arXiv:1808.07187.
Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2017, November). Neural question generation from text: A preliminary study. In National CCF Conference on Natural Language Processing and Chinese Computing (pp. 662–671). Cham, Switzerland: Springer.
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The work was supported in part by the National Natural Science Foundation of China [Grant Number 61807004].
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Zhang, L. (2020). Mapping Machine-Generated Questions to Their Related Paragraphs in the Textbook. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_14
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