[1] Nan Duan, Duyu Tang. Overview of the NLPCC-2017 shared task: Open domain Chinese question answering[C]//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing, 2017.
[2] Guokun Lai, Qizhe Xie, Hanxiao Liu,et al. Race: Large-scale reading comprehension dataset from examinations[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 785-794.
[3] Mitkov Ruslan, Ha Le An. Computer-aided generation of multiple-choice tests[C]//Proceedings of the HLTNAACL 03 Workshop on Building Educational Applications using Natural Language Processing, 2003.
[4] Michael Heilman, Noah A Smith. Good question! Statistical ranking for question generation [C]//Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010: 609-617.
[5] David Lindberg, Fred Popowich, John Nesbit, et al. Generating natural language questions to support learning on-line[C]//Proceedings of the 14th European Workshop on Natural Language Generation, 2013: 105-114.
[6] Vasile Rus, Brendan Wyse, Paul Piwek,et al. A detailed account of the first question generation shared task evaluation challenge [J]. Dialogue and Discourse, 2012, 3(2):177-204.
[7] Yifan Gao, Lidong Bing, Piji Li, et al. Generating distractors for reading comprehension questions from real examinations[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence,2019: 6423-6430.
[8] Xiaorui Zhou, Senlin Luo, Yunfang Wu. Co-attention hierarchical network: Generating coherent long distractors for reading comprehension[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 9725-9732.
[9] Guanliang Chen, Jie Yang, Claudia Hauff, et al. LearningQ: A Large-scale dataset for educational question generation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence,2018.
[10] Angelica Willis, Glenn Davis, Sherry Ruan, et al. Key phrase extraction for generating educational question-answer pairs[C]//Proceedings of the 6th ACM Conference on Learning@scale, 2019.
[11] Duyu Tang, Nan Duan, Tao Qin, et al. Question answering and question generation as dual tasks[EB/OL]. CoRR, abs/1706.02027, 2017.
[12] Nan Duan, Duyu Tang,Peng Chen, et al. Question generation for question answering[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017: 866-874.
[13] Shiyue Zhang and Mohit Bansal. Addressing semantic drift in question generation for semi-supervised question answering [C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019: 2495-2509.
[14] Yansen Wang, Chenyi Liu, Minlie Huang, et al. Learning to ask questions in open domain conversational systems with typed decoders[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018:2193-2203.
[15] Aliannejadi Mohammad, Zamani Hamed, Crestani Fabio, et al. Asking clarifying questions in open-domain information-seeking conversations[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019.
[16] George A Miller.WordNet: An electronic lexical database[M]. MIT Press, 1998.
[17] Dhole K D, Manning C D. Syn-QG: Syntactic and shallow semantic rules for question generation[C]//Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics, 2020.
[18] Karin Kipper Schuler. VerbNet: Abroad coverage, comprehensive verb lexicon [D]. Ph D. Thesis, University of Pennsylvania, 2005.
[19] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev,et al. Squad: 100,000+ questions for machine comprehension of text[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
[20] Tri Nguyen, Mir Rosenberg, Xia Song,et al. Ms marco: A human generated machine reading comprehension dataset[J/OL]. ArXiv, preprint arXiv: 1611.09268, 2016.
[21] Xinya Du, Junru Shao, Claire Cardie. Learning to ask: Neural question generation for reading comprehension[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017:1342-1352.
[22] Qingyu Zhou, Nan Yang, Furu Wei,et al. Neural question generation from text: A preliminary study [C]//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing, 2017:662-671.
[23] Xingdi Yuan, Tong Wang, Caglar Gulcehre, Aless. Machine comprehension by text-to-text neural question generation [C]//Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017: 15-25.
[24] Linfeng Song, Zhiguo Wang, Wael Hamza,et al. Leveraging context information for natural question generation[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, 2018:569-574.
[25] Abigail See, Peter J Liu, Christopher D Manning. Get to the point: Summarization with pointer-generator networks[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017:1073-1083.
[26] Xingwu Sun, Jing Liu, Yajuan Lyu,et al. Answer-focused and position-aware neural question generation[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018: 3930- 3939.
[27] 董孝政, 洪宇, 朱芬红, 等. 基于密令位置信息特征的问题生成[J].中文信息学报2019, 33(8): 93-100.
[28] Yanghoon Kim, Hwanhee Lee, Joongbo Shin, et al. Improving neural question generation using answer separation[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence,2019:6602-6609.
[29] Bang Liu, Mingjun Zhao, Di Niu,et al. Learning to generate questions by learning what not to generate[C]//Proceedings of the Web Conference, 2019:1106-1118.
[30] Wenjie Zhou, Minghua Zhang, Yunfang Wu. Multi-task learning with language modeling for question generation[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019:3392-3397.
[31] Wenjie Zhou, Minghua Zhang, Yunfang Wu. Question-type driven question generation [C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019:6031-6036.
[32] Jingjing Li, Yifan Gao, Lidong Bing,et al. Improving question generation with to the point context[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019:3216-3226.
[33] Xiyao Ma, Qile Zhu, Yanlin Zhou, et al. Improving question generation with sentence-level semantic matching and answer position inferring[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
[34] Xin Jia, Wenjie Zhou, Xu Sun, et al. How to ask good questions? Try to leverage paraphrases[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020:6130-6140.
[35] Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, et al. Paragraph-level neural question generation with maxout pointer and gated self-attention networks[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018:3901-3910.
[36] Preksha Nema, Akash Kumar Mohankumar, Mitesh M Khapra, et al. Let’s ask again: Refine network for automatic question generation[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019:3314-3323.
[37] Yu Chen, LingfeiWu, Mohammed J Zaki. Natural question generation with reinforcement learning based graph-to-sequence model[C]//Proceedings of the 33rd Conference on Neural Information Processing Systems, 2019.
[38] Luu Anh Tuan, Darsh J Shah, Regina Barzilay. Capturing greater context for question generation[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
[39] Wang W, Wei F, Dong L, et al. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers[EB/OL]. ArXiv abs/2002.10957, 2020.
[40] Bao H, Dong L, Wei F, et al. UniLMv2: Pseudo-masked language models for unied language model pre-training[J/OL]. ArXiv preprint arXiv: 2002.12804. 2020.
[41] Yan Y, Qi W, Gong Y, et al. ProphetNet: predicting future n-gram for sequence-to-sequence pre-training[C]//Proceedings of the Association for Computational Linguistics: EMNLP 2020:2401-2410.
[42] Xiao D, Zhang H, Li Y, et al. ERNIE-GEN: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence, 2020:3997-4003.
[43] Jinwen Ma, Wenpeng Hu, Bing Liu,et al. Aspect-based question generation[C]//Proceedings of the International Conference on Learning Representations, 2018.
[44] Siyuan Wang, Zhongyu Wei, Zhihao Fan,et al.A multi-agent communication framework for question-worthy phrase extraction and question generation[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence,2019:7168-7175.
[45] Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano. Self-attention architectures for answer-agnostic neural question generation[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2019:6027-6032.
[46] Xinya Du, Claire Cardie. Identifying where to focus in reading comprehension for neural question generation[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017:2067-2073.
[47] Sandeep Subramanian, Wang Tong, XingdiYuan, et al. Neural models for key phrase detection and question generation[C]//Proceedings of the Workshop on Machine Reading for Question Answering, 2017:78-88.
[48] Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee,et al. Cross-lingual training for automatic question generation[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2019:4863-4872.
[49] Wei He, Kai Liu, Jing Liu,et al. DuReader: A Chinese machine reading comprehension dataset from real-world applications[C]//Proceedings of the Workshop on Machine Reading for Question Answering, 2018: 37-46.
[50] Zewen Chi, Li Dong, Furu Wei, et al. Cross-lingual natural language generation via pre-training[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
[51] Peng Li, Wei Li, Zhengyan He,et al.Dataset and neural recurrent sequence labeling model for open-domain factoid question answering[EB/OL]. arXiv:1607.06275. 2016.
[52] Kishore Papineni, Salim Roukos, ToddWard, et al. BLEU: A method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002:311-318.
[53] Michael Denkowski, Alon Lavie. Meteor universal: Language specific translation evaluation for any target language[C]//Proceedings of the 9th Workshop on Statistical Machine Translation. Association for Computational Linguistics, 2014: 376- 380.
[54] Chin Yew Lin.Rouge: A package for automatic evaluation of summaries[C]//Proceedings of Text Summarization Workshop of the 42th Annual Meeting of the Association for Computational Linguistics, 2004:74-81.
[55] Preksha Nema, Mitesh M Khapra. Towards a better metric for evaluating question generation systems[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018.
[56] Md Arafat Sultan, Shubham Chandel, Ramón F Astudillo, et al. On the importance of diversity in question generation for QA[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.
[57] Qian Yu, Lidong Bing, Qiong Zhang, et al. Review-based question generation with adaptive instance transfer and augmentation[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020:280-290.
[58] Pan L, Xie Y, Feng Y, et al. Semantic graphs for generating deep questions[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.
[59] Ko W J, Chen T Y, Huang Y, et al. Inquisitive question generation for high level text comprehension[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020:6544-6555.