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Association for Computational Linguistics","DOI":"10.18653\/v1\/2020.aacl-main.78"},{"issue":"1","key":"10.1007\/s40593-023-00374-x_bib58","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s40593-019-00186-y","article-title":"A systematic review of automatic question generation for educational purposes","volume":"30","author":"Kurdi","year":"2020","journal-title":"International Journal of Artificial Intelligence in Education"},{"key":"10.1007\/s40593-023-00374-x_bib59","doi-asserted-by":"crossref","unstructured":"Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E. (2017). RACE: Large-scale ReAding comprehension dataset from examinations. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 785\u2013794). 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Stay hungry, stay focused: Generating informative and specific questions in information-seeking conversations. arXiv:2004.14530 [cs.CL]","DOI":"10.18653\/v1\/2020.findings-emnlp.3"},{"key":"10.1007\/s40593-023-00374-x_bib78","doi-asserted-by":"crossref","unstructured":"Qu, F., Jia, X., Wu, Y. (2021). Asking questions like educational experts: Automatically generating question-answer pairs on real-world examination data. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 2583\u20132593). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.emnlp-main.202"},{"key":"10.1007\/s40593-023-00374-x_bib79","doi-asserted-by":"crossref","unstructured":"Reddy, S., Raghu, D., Khapra, M.M., Joshi, S. (2017). Generating natural language Question-Answer pairs from a knowledge graph using a RNN based question generation model. In Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 1, long papers (pp. 376\u2013385). Association for Computational Linguistics.","DOI":"10.18653\/v1\/E17-1036"},{"key":"10.1007\/s40593-023-00374-x_bib80","unstructured":"Renaud, R., & Murray, H. (2003). The effect of higher-order questions on critical thinking skills. In Annual meeting of the american educational research association"},{"key":"10.1007\/s40593-023-00374-x_bib81","unstructured":"Rice University (1999). OpenStax. https:\/\/openstax.org\/. (Accessed 1 June 2022)"},{"issue":"2","key":"10.1007\/s40593-023-00374-x_bib82","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s10579-021-09545-5","article-title":"Towards the benchmarking of question generation: introducing the monserrate corpus","volume":"56","author":"Rodrigues","year":"2021","journal-title":"Language Resources and Evaluation"},{"key":"10.1007\/s40593-023-00374-x_bib83","doi-asserted-by":"crossref","unstructured":"Sekuli\u0107, I., Aliannejadi, M., Crestani, F. (2021). Towards Facet-Driven generation of clarifying questions for conversational search. In Proceedings of the 2021 ACM SIGIR international conference on theory of information retrieval (pp. 167\u2013175). Association for Computing Machinery.","DOI":"10.1145\/3471158.3472257"},{"key":"10.1007\/s40593-023-00374-x_bib84","doi-asserted-by":"crossref","unstructured":"Serban, I.V., Garc\u00eda-Dur\u00e1n, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., Bengio, Y. (2016). Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers) (pp. 588\u2013598). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P16-1056","DOI":"10.18653\/v1\/P16-1056"},{"key":"10.1007\/s40593-023-00374-x_bib85","doi-asserted-by":"crossref","unstructured":"Shi, B., Li, S., Yang, J., Kazdagli, M.E., He, Q. (2020). Learning to ask screening questions for job postings. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 549\u2013558). Association for Computing Machinery.","DOI":"10.1145\/3397271.3401118"},{"key":"10.1007\/s40593-023-00374-x_bib86","doi-asserted-by":"crossref","unstructured":"Shimmei, M., & Matsuda, N. (2021). Learning association between learning objectives and key concepts to generate pedagogically valuable questions. In Artificial intelligence in education (pp. 320\u2013324). Springer International Publishing.","DOI":"10.1007\/978-3-030-78270-2_57"},{"key":"10.1007\/s40593-023-00374-x_bib87","doi-asserted-by":"crossref","unstructured":"Singh, H., Nasery, A., Mehta, D., Agarwal, A., Lamba, J., Srinivasan, B.V. (2021). MIMOQA: Multimodal input multimodal output question answering. In Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: Human language technologies (pp. 5317\u20135332). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.naacl-main.418"},{"key":"10.1007\/s40593-023-00374-x_bib88","doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C. (2016). ConceptNet 5.5: An open multilingual graph of general knowledge. arXiv:1612.03975 [cs.CL]","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"10.1007\/s40593-023-00374-x_bib89","doi-asserted-by":"crossref","unstructured":"Srivastava, M., & Goodman, N. (2021). Question generation for adaptive education. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 2: Short papers) (pp. 692\u2013701). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.acl-short.88"},{"key":"10.1007\/s40593-023-00374-x_bib90","unstructured":"Stasaski, K., Rathod, M., Tu, T., Xiao, Y., Hearst, M.A. (2021). Automatically generating Cause-and-Effect questions from passages. In Proceedings of the 16th workshop on innovative use of NLP for building educational applications (pp. 158\u2013170)"},{"key":"10.1007\/s40593-023-00374-x_bib91","unstructured":"Steuer, T., Filighera, A., Meuser, T., Rensing, C. (2021, October). I do not understand what I cannot define: Automatic question generation with Pedagogically-Driven content selection. arXiv:2110.04123 [cs.CL]"},{"key":"10.1007\/s40593-023-00374-x_bib92","doi-asserted-by":"crossref","unstructured":"Steuer, T., Filighera, A., Rensing, C. (2020). Remember the facts? investigating Answer-Aware neural question generation for text comprehension. In Artificial intelligence in education (pp. 512\u2013523). Springer International Publishing.","DOI":"10.1007\/978-3-030-52237-7_41"},{"key":"10.1007\/s40593-023-00374-x_bib93","doi-asserted-by":"crossref","unstructured":"Stewart, I., & Mihalcea, R. (2021). How well do you know your audience? reader-aware question generation. arXiv:2110.08445 [cs.CL]","DOI":"10.18653\/v1\/2022.sigdial-1.27"},{"key":"10.1007\/s40593-023-00374-x_bib94","doi-asserted-by":"crossref","unstructured":"Su, M.-H., Wu, C.-H., Huang, K.-Y., Hong, Q.-B., Huang, H.-H. (2018). Follow-up question generation using pattern-based seq2seq with a small corpus for interview coaching. In INTERSPEECH (pp. 1006\u20131010). isca-speech.org.","DOI":"10.21437\/Interspeech.2018-1007"},{"key":"10.1007\/s40593-023-00374-x_bib95","doi-asserted-by":"crossref","unstructured":"Sultan, M.A., Chandel, S., Astudillo, R.F., Castelli, V. (2020). On the importance of diversity in question generation for QA. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 5651\u20135656)","DOI":"10.18653\/v1\/2020.acl-main.500"},{"key":"10.1007\/s40593-023-00374-x_bib96","doi-asserted-by":"crossref","unstructured":"Sun, X., Liu, J., Lyu, Y., He, W., Ma, Y., Wang, S. (2018). Answer-focused and position-aware neural question generation. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 3930\u20133939)","DOI":"10.18653\/v1\/D18-1427"},{"issue":"1","key":"10.1007\/s40593-023-00374-x_bib97","doi-asserted-by":"crossref","first-page":"82","DOI":"10.2307\/747722","article-title":"Text explicitness and inferential questioning: Effects on story understanding and recall","volume":"22","author":"Sundbye","year":"1987","journal-title":"Reading Research Quarterly"},{"key":"10.1007\/s40593-023-00374-x_bib98","doi-asserted-by":"crossref","unstructured":"Syed, R., Collins-Thompson, K., Bennett, P.N., Teng, M., Williams, S., Tay, D.W.W., Iqbal, S. (2020). Improving learning outcomes with gaze tracking and automatic question generation. In Proceedings of the web conference 2020 (pp. 1693\u20131703). New York, NY, USA: Association for Computing Machinery","DOI":"10.1145\/3366423.3380240"},{"key":"10.1007\/s40593-023-00374-x_bib99","unstructured":"Talmor, A., Yoran, O., Catav, A., Lahav, D., Wang, Y., Asai, A., Berant, J. (2021). Multimodalqa: complex question answering over text, tables and images. International conference on learning representations"},{"issue":"7","key":"10.1007\/s40593-023-00374-x_bib100","doi-asserted-by":"crossref","first-page":"155","DOI":"10.5688\/ajpe777155","article-title":"Best practice strategies for effective use of questions as a teaching tool","volume":"77","author":"Tofade","year":"2013","journal-title":"American Journal of Pharmaceutical Education"},{"key":"10.1007\/s40593-023-00374-x_bib101","doi-asserted-by":"crossref","unstructured":"Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., Suleman, K. (2016). NewsQA: A machine comprehension dataset. arXiv:1611.09830 [cs.CL]","DOI":"10.18653\/v1\/W17-2623"},{"key":"10.1007\/s40593-023-00374-x_bib102","doi-asserted-by":"crossref","unstructured":"Tuan, L.A., Shah, D., Barzilay, R. (2020). Capturing greater context for question generation. In Proceedings of the aaai conference on artificial intelligence (vol. 34, pp. 9065\u20139072).","DOI":"10.1609\/aaai.v34i05.6440"},{"key":"10.1007\/s40593-023-00374-x_bib103","doi-asserted-by":"crossref","unstructured":"Wang, A., Cho, K., Lewis, M. (2020). Asking and answering questions to evaluate the factual consistency of summaries. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 5008\u20135020). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.450"},{"key":"10.1007\/s40593-023-00374-x_bib104","doi-asserted-by":"crossref","unstructured":"Wang, S., Wei, Z., Fan, Z., Huang, Z., Sun, W., Zhang, Q., Huang, X. (2020). PathQG: Neural question generation from facts. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp. 9066\u20139075). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.emnlp-main.729"},{"key":"10.1007\/s40593-023-00374-x_bib105","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, C., Huang, M., Nie, L. (2018). Learning to ask questions in open-domain conversational systems with typed decoders. In Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: Long papers) (pp. 2193\u20132203). Association for Computational Linguistics.","DOI":"10.18653\/v1\/P18-1204"},{"key":"10.1007\/s40593-023-00374-x_bib106","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lan, A., Baraniuk, R. (2021). Math word problem generation with mathematical consistency and problem context constraints. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 5986\u20135999). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.emnlp-main.484"},{"key":"10.1007\/s40593-023-00374-x_bib107","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lan, A.S., Nie, W., Waters, A.E., Grimaldi, P.J., Baraniuk, R.G. (2018). QG-net: a data-driven question generation model for educational content. In Proceedings of the fifth annual ACM conference on learning at scale (p. 7). ACM.","DOI":"10.1145\/3231644.3231654"},{"key":"10.1007\/s40593-023-00374-x_bib108","doi-asserted-by":"crossref","unstructured":"Welbl, J., Liu, N.F., Gardner, M. (2017). Crowdsourcing multiple choice science questions. In Proceedings of the 3rd workshop on noisy user-generated text (pp. 94\u2013106). Association for Computational Linguistics.","DOI":"10.18653\/v1\/W17-4413"},{"key":"10.1007\/s40593-023-00374-x_bib109","doi-asserted-by":"crossref","unstructured":"White, J., Poesia, G., Hawkins, R., Sadigh, D., Goodman, N. (2021). Opendomain clarification question generation without question examples. In Proceedings of the 2021 conference on empirical methods in natural language processing (pp. 563\u2013570). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.emnlp-main.44"},{"key":"10.1007\/s40593-023-00374-x_bib110","doi-asserted-by":"crossref","unstructured":"Willis, A., Davis, G., Ruan, S., Manoharan, L., Landay, J., Brunskill, E. (2019). Key phrase extraction for generating educational Question-Answer pairs. In Proceedings of the sixth (2019) ACM conference on learning @ scale (pp. 1\u201310). Association for Computing Machinery.","DOI":"10.1145\/3330430.3333636"},{"key":"10.1007\/s40593-023-00374-x_bib111","doi-asserted-by":"crossref","unstructured":"Xiao, D., Zhang, H., Li, Y., Sun, Y., Tian, H., Wu, H., Wang, H. (2020). ERNIE-GEN: An enhanced multi-flow pre-training and finetuning framework for natural language generation. In Proceedings of the Twenty-Ninth international joint conference on artificial intelligence. International Joint Conferences on Artificial Intelligence Organization.","DOI":"10.24963\/ijcai.2020\/553"},{"key":"10.1007\/s40593-023-00374-x_bib112","unstructured":"Xin, J., Hao, W., Dawei, Y., Yunfang, W. (2021). Enhancing question generation with commonsense knowledge. In Proceedings of the 20th chinese national conference on computational linguistics (pp. 976\u2013987). Chinese Information Processing Society of China."},{"key":"10.1007\/s40593-023-00374-x_bib113","doi-asserted-by":"crossref","unstructured":"Yang, Z., Hu, J., Salakhutdinov, R., Cohen, W. (2017). Semi-Supervised QA with generative Domain-Adaptive nets. In Proceedings of the 55th annual meeting of the association for computational linguistics (vol. 1: Long papers) (pp. 1040\u20131050). Vancouver, Canada: Association for Computational Linguistics.","DOI":"10.18653\/v1\/P17-1096"},{"key":"10.1007\/s40593-023-00374-x_bib114","doi-asserted-by":"crossref","unstructured":"Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., Manning, C.D. (2018). HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 2369\u20132380). Association for Computational Linguistics.","DOI":"10.18653\/v1\/D18-1259"},{"key":"10.1007\/s40593-023-00374-x_bib115","doi-asserted-by":"crossref","unstructured":"Yao, B., Wang, D., Wu, T., Hoang, T., Sun, B., Li, T.J.-J., Xu, Y. (2022). It is AI\u2019s turn to ask humans a question: Question-Answer pair generation for children\u2019s story books. In Proceedings of the 60th annual meeting of the association for computational linguistics (vol. 1: Long papers) (pp. 731\u2013744). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2022.acl-long.54"},{"key":"10.1007\/s40593-023-00374-x_bib116","series-title":"A survey of Knowledge-Enhanced text generation","author":"Yu","year":"2022"},{"key":"10.1007\/s40593-023-00374-x_bib117","doi-asserted-by":"crossref","unstructured":"Yu, X., & Jiang, A. (2021). Expanding, retrieving and infilling: Diversifying Cross-Domain question generation with flexible templates. In Proceedings of the 16th conference of the european chapter of the association for computational linguistics: Main volume (pp. 3202\u20133212). Association for Computational Linguistics.","DOI":"10.18653\/v1\/2021.eacl-main.279"},{"key":"10.1007\/s40593-023-00374-x_bib118","doi-asserted-by":"crossref","unstructured":"Yuan, W., Yin, H., He, T., Chen, T., Wang, Q., Cui, L. (2022). Unified question generation with continual lifelong learning. arXiv:2201.09696 [cs.CL]","DOI":"10.1145\/3485447.3511930"},{"key":"10.1007\/s40593-023-00374-x_bib119","unstructured":"Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Ahmed, A. (2020, July). Big bird: Transformers for longer sequences. arXiv:2007.14062 [cs.LG]"},{"issue":"1","key":"10.1007\/s40593-023-00374-x_bib120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1330295.1330298","article-title":"A review on question generation from natural language text","volume":"40","author":"Zhang","year":"2021","journal-title":"ACM Transactions on Information and System Security"},{"key":"10.1007\/s40593-023-00374-x_bib121","doi-asserted-by":"crossref","unstructured":"Zhang, Z., & Zhu, K. (2021). Diverse and specific clarification question generation with keywords. In Proceedings of the web conference 2021 (pp. 3501\u20133511). Association for Computing Machinery.","DOI":"10.1145\/3442381.3449876"},{"key":"10.1007\/s40593-023-00374-x_bib122","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ni, X., Ding, Y., Ke, Q. (2018). Paragraph-level neural question generation with maxout pointer and gated self-attention networks. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 3901\u20133910).","DOI":"10.18653\/v1\/D18-1424"},{"key":"10.1007\/s40593-023-00374-x_bib123","doi-asserted-by":"crossref","unstructured":"Zhou, Q., & Huang, D. (2019). Towards generating math word problems from equations and topics. In Proceedings of the 12th international conference on natural language generation (pp. 494\u2013503).","DOI":"10.18653\/v1\/W19-8661"},{"key":"10.1007\/s40593-023-00374-x_bib124","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., Zhou, M. (2017). Neural question generation from text: A preliminary study. In Natural language processing and chinese computing (pp. 662\u2013671). 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