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QuestionGenerationPapers

KBQG

2014-2019

  1. How Question Generation Can Help Question Answering over Knowledge Base. Hu S, Zou L, Zhu Z. NLPCC, 2019. paper

  2. Difficulty-controllable Multi-hop Question Generation From Knowledge Graphs. Vishwajeet Kumar, Yuncheng Hua, Ganesh Ramakrishnan, et al. EMNLP, 2019. paper code&dataset

  3. Difficulty Controllable Generation of Reading Comprehension Questions. Gao Y, Bing L, Chen W, et al. IJCAI, 2019. paper

  4. Improving Neural Question Generation using World Knowledge. Gupta D, Suleman K, Adada M, et al. arXiv, 2019. paper

  5. Leveraging knowledge bases in lstms for improving machine reading. Yang B, Mitchell T. arXiv, 2019. paper

  6. Automatic Question Generation based on MOOC Video Subtitles and Knowledge Graph. Ma L, Ma Y. ACM, 2019. paper

  7. Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types. Hady Elsahar, Christophe Gravier, Frederique Laforest. NAACL, 2018. paper code

  8. A Neural Question Generation System Based on Knowledge Base Wang H, Zhang X, Wang H. NLPCC, 2018. paper

  9. Automatic Generation of Multiple Choice Questions from Slide Content using Linked Data. Faizan A, Lohmann S. ACM, 2018. paper

  10. Formal query generation for question answering over knowledge bases. Zafar H, Napolitano G, Lehmann J. ESWC, 2018. paper

  11. Difficulty-level Modeling of Ontology-based Factual Questions. Venugopal V E, Kumar P S. Semantic Web journal, 2017. paper

  12. Generating natural language question-answer pairs from a knowledge graph using a rnn based question generation model. Reddy S, Raghu D, Khapra M M, et al. ACL, 2017. paper

  13. Knowledge Questions from Knowledge Graphs. Seyler D, Yahya M, Berberich K. ACM SIGIR, 2017. paper

  14. Web authoriser tool to build assessments using Wikipedia articles. Adithya S S R, Singh P K. IEEE, 2017. paper

  15. Domain-specific question generation from a knowledge base. Song L, Zhao L. arXiv, 2016. paper dataset

  16. Question Generation from a Knowledge Base with Web Exploration. Song L, Zhao L. arXiv, 2016. paper

  17. Ontology-based multiple choice question generation. Alsubait T, Parsia B, Sattler U. KI-Künstliche Intelligenz, 2016. paper

  18. Towards natural language question generation for the validation of ontologies and mappings. Abacha A B, Dos Reis J C, Mrabet Y, et al. BS, 2016. paper

  19. Generating Quiz Questions from knowledge graphs. Seyler D, Yahya M, Berberich K. WWW, 2015. paper

  20. A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption. Web Semantics: Science, Services and Agents on the World Wide Web, 2015. paper

  21. Question generation from a knowledge base. Chaudhri V K, Clark P E, Overholtzer A, et al. KEKM, 2014. paper

  22. Generating multiple choice questions from ontologies lessons learnt. Alsubait T, Parsia B, Sattler U. OWLED, 2014. paper

  23. Generating Multiple Choice Questions From Ontologies: How Far Can We Go? Alsubait T, Parsia B, Sattler U. EKAW, 2014. paper

2008-2013

  1. A similarity-based theory of controlling MCQ difficulty. Tahani Alsubait, Bijan Parsia, Ulrike Sattler IEEE, 2013. paper

  2. Automatic generation of multiple choice questions using wikipedia. Bhatia A S, Kirti M, Saha S K. PReMI, 2013. paper

  3. Question Difficulty Estimation in Community Question Answering Services. Liu J, Wang Q, Lin C Y, et al. EMNLP, 2013. paper

  4. Question generation from concept maps. Olney A M, Graesser A C, Person N K. Dialogue & Discourse, 2012. paper

  5. Using wikipedia and conceptual graph structures to generate questions for academic writing support. Liu M, Calvo R A, Aditomo A, et al. IEEE, 2012. paper

  6. Automatic Generation Of Multiple Choice Questions From Domain Ontologies. Papasalouros A, Kanaris K, Kotis K. e-Learning, 2008. paper

2014-2019

  1. Unified Language Model Pre-training for Natural Language Understanding and Generation. Dong L, Yang N, Wang W, et al. NIPS, 2019. paper

  2. Question-type Driven Question Generation. Zhou W, Zhang M, Wu Y. EMNLP, 2019. paper

  3. Multi-Task Learning with Language Modeling for Question Generation. Zhou W, Zhang M, Wu Y. arXiv, 2019. paper

  4. Difficulty Controllable Generation of Reading Comprehension Questions. Yifan Gao, Lidong Bing, Wang Chen, et al. IJCAI, 2019. paper

  5. SAC-Net: Stroke-Aware Copy Network for Chinese Neural Question Generation. Li W, Kang Q, Xu B, et al. IEEE, 2019. paper

  6. Distant Supervised Why-Question Generation with Passage Self-Matching Attention. Hu J, Li Z, Wu R, et al. IJCNN, 2019. paper

  7. Generating Question-Answer Hierarchies. Kalpesh Krishna and Mohit Iyyer. ACL, 2019. paper code

  8. Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling. Yifan Gao, Piji Li, Irwin King, et al. ACL, 2019. paper code

  9. Cross-Lingual Training for Automatic Question Generation. Kumar V, Joshi N, Mukherjee A, et al. ACL, 2019. paper dataset

  10. Multi-hop Reading Comprehension through Question Decomposition and Rescoring. Sewon Min, Victor Zhong, Luke Zettlemoyer, et al. ACL, 2019. paper

  11. Learning to Ask Unanswerable Questions for Machine Reading Comprehension. Haichao Zhu, Li Dong, Furu Wei, et al. ACL, 2019.

  12. Reinforced Dynamic Reasoning for Conversational Question Generation. Boyuan Pan, Hao Li, Ziyu Yao, et al. ACL, 2019. paper code dataset

  13. Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums. Zi Chai, Xinyu Xing, Xiaojun Wan, et al. ACL, 2019.

  14. Self-Attention Architectures for Answer-Agnostic Neural Question Generation. Thomas Scialom, Benjamin Piwowarski and Jacopo Staiano. ACL, 2019.

  15. Evaluating Rewards for Question Generation Models. Tom Hosking and Sebastian Riedel. NAACL, 2019. paper

  16. Difficulty controllable question generation for reading comprehension. Gao Y, Wang J, Bing L, et al. IJCAI, 2019. paper

  17. Weak Supervision Enhanced Generative Network for Question Generation. Yutong Wang, Jiyuan Zheng, Qijiong Liu, et al. IJCAI, 2019. paper

  18. Answer-based Adversarial Training for Generating Clarification Questions. Rao S, Daumé III H. NAACL, 2019. paper code

  19. Information Maximizing Visual Question Generation. Krishna, Ranjay, Bernstein, Michael, Fei-Fei, Li. arXiv, 2019. paper

  20. Learning to Generate Questions by Learning What not to Generate. Liu B, Zhao M, Niu D, et al. WWW, 2019. paper

  21. Joint Learning of Question Answering and Question Generation. Sun Y, Tang D, Duan N, et al. IEEE, 2019. paper dataset

  22. Domain-specific question-answer pair generation. Beason W A, Chandrasekaran S, Gattiker A E, et al. Google Patents, 2019. paper

  23. Anaphora Reasoning Question Generation Using Entity Coreference. Hasegawa, Kimihiro, Takaaki Matsumoto, and Teruko Mitamura. 2019. paper

  24. Improving Neural Question Generation using Answer Separation. Kim Y, Lee H, Shin J, et al. AAAI, 2019. paper

  25. A novel framework for Automatic Chinese Question Generation based on multi-feature neural network mode. Zheng H T, Han J, Chen J Y, et al. Comput. Sci. Inf. Syst., 2018. paper

  26. Automatic opinion question generation. Chali Y, Baghaee T. ICNLG, 2018. paper

  27. Visual question generation as dual task of visual question answering. Li Y, Duan N, Zhou B, et al. IEEE, 2018. paper

  28. Aspect-based question generation. Hu W, Liu B, Ma J, et al. ICLR, 2018. paper

  29. QG-net: a data-driven question generation model for educational content. Wang Z, Lan A S, Nie W, et al. ACM, 2018. paper

  30. Answer-focused and Position-aware Neural Question Generation. Sun X, Liu J, Lyu Y, et al. EMNLP, 2018. paper

  31. Automatic Question Generation using Relative Pronouns and Adverbs. Khullar P, Rachna K, Hase M, et al. ACL, 2018. paper

  32. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information Rao S, Daumé III H. arXiv, 2018. paper dataset

  33. Soft layer-specific multi-task summarization with entailment and question generation. Guo H, Pasunuru R, Bansal M. arXiv, 2018. paper

  34. Leveraging context information for natural question generation Song L, Wang Z, Hamza W, et al. ACL, 2018. paper code

  35. Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders. Wang Y, Liu C, Huang M, et al. arXiv, 2018. paper code dataset

  36. Did the model understand the question? Mudrakarta P K, Taly A, Sundararajan M, et al. arXiv, 2018. paper code dataset

  37. Know What You Don't Know: Unanswerable Questions for SQuAD. Rajpurkar P, Jia R, Liang P. arXiv, 2018. paper code&dataset

  38. Paragraph-level neural question generation with maxout pointer and gated self-attention networks. Zhao Y, Ni X, Ding Y, et al. EMNLP, 2018. paper

  39. Harvesting paragraph-level question-answer pairs from wikipedia. Du X and Cardie C. arXiv, 2018. paper code&dataset

  40. Teaching Machines to Ask Questions. Kaichun Yao, Libo Zhang, Tiejian Luo, et al. IJCAI, 2018. paper

  41. Question Generation using a Scratchpad Encoder. Benmalek R Y, Khabsa M, Desu S, et al. 2018. paper

  42. Learning to collaborate for question answering and asking. Tang D, Duan N, Yan Z, et al. NAACL, 2018. paper

  43. A Question Type Driven Framework to Diversify Visual Question Generation Zhihao Fan, Zhongyu Wei, Piji Li, et al. IJCAI,2018. paper

  44. Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features. Harrison V, Walker M. arXiv,2018. paper

  45. Learning to Ask: Neural Question Generation for Reading Comprehension. Xinya Du, Junru Shao, Claire Cardie. ACL, 2017. paper code

  46. Neural question generation from text: A preliminary study. Zhou Q, Yang N, Wei F, et al. NLPCC, 2017. paper

  47. Question answering and question generation as dual tasks. Tang D, Duan N, Qin T, et al. arXiv, 2017. paper

  48. A syntactic approach to domain-specific automatic question generation. Danon G, Last M. arXiv, 2017. paper

  49. Creativity: Generating diverse questions using variational autoencoders. Jain U, Zhang Z, Schwing A G. IEEE,2017. paper

  50. Automatic chinese factual question generation. Liu M, Rus V, Liu L. IEEE, 2016. paper

  51. A joint model for question answering and question generation. Wang, Tong, Xingdi Yuan, and Adam Trischler. arXiv, 2017. paper

  52. Neural models for key phrase detection and question generation. Subramanian S, Wang T, Yuan X, et al. arXiv, 2017. paper

  53. Machine comprehension by text-to-text neural question generation. Yuan X, Wang T, Gulcehre C, et al. arXiv, 2017. paper

  54. Question generation for question answering. Duan N, Tang D, Chen P, et al. EMNLP,2017. paper

  55. Ranking automatically generated questions using common human queries. Chali Y, Golestanirad S. INLG, 2016. paper

  56. Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. Serban I V, García-Durán A, Gulcehre C, et al. arXiv, 2016. paper dataset

  57. Towards Topic-to-Question Generation. XYllias Chali, Sadid A. Hasan. Computational Linguistics, 2015. paper

  58. Literature review of automatic question generation systems. Rakangor, Sheetal, and Y. Ghodasara. International Journal of Scientific and Research Publications,2015. paper

  59. Revup: Automatic gap-fill question generation from educational texts. Kumar G, Banchs R and D'Haro L F. ACL, 2015. paper

  60. Deep questions without deep understanding. Labutov I, Basu S and Vanderwende L. ACL, 2015. paper

  61. Ontology-based multiple choice question generation. Al-Yahya, Maha. The Scientific World Journal, 2014. paper

  62. Linguistic considerations in automatic question generation. Mazidi, Karen, and Rodney D. Nielsen. ACL, 2014. paper

  63. Automatic question generation for educational applications–the state of art. Le, Nguyen-Thinh, Tomoko Kojiri, and Niels Pinkwart. ACMKE, 2014. paper

2008-2013

  1. Generating natural language questions to support learning on-line. Lindberg D, Popowich F, Nesbit J, et al. ENLG, 2013. paper

  2. Question generation for French: collating parsers and paraphrasing questions. Bernhard, Delphine, et al. Dialogue & Discourse,2012. paper dataset1 dataset2

  3. Question generation from concept maps. Olney A M, Graesser A C, Person N K. Dialogue & Discourse, 2012. paper

  4. Towards automatic topical question generation. Chali, Yllias, and Sadid A. Hasan. COLING,2012. paper dataset

  5. Question generation based on lexico-syntactic patterns learned from the web. Curto, Sérgio, Ana Cristina Mendes, and Luisa Coheur. Dialogue & Discourse,2012. paper

  6. G-Asks: An intelligent automatic question generation system for academic writing support. Liu, Ming, Rafael A. Calvo, and Vasile Rus. Dialogue & Discourse, 2012. paper

  7. Semantics-based question generation and implementation. Yao, Xuchen, Gosse Bouma, and Yi Zhang. Dialogue & Discourse,2012. paper system dataset1 dataset2 dataset3 dataset4

  8. Mind the gap: learning to choose gaps for question generation. Becker, Lee, Sumit Basu, and Lucy Vanderwende. NAACL,2012. paper dataset

  9. OntoQue: a question generation engine for educational assesment based on domain ontologies. Al-Yahya, Maha. IEEE, 2011. paper

  10. Automatic gap-fill question generation from text books. Agarwal M, Mannem P. the 6th Workshop on Innovative Use of NLP for Building Educational Applications,2011. paper

  11. Automatic question generation using discourse cues. Agarwal, Manish, Rakshit Shah, and Prashanth Mannem. the 6th Workshop on Innovative Use of NLP for Building Educational Applications,2011. paper

  12. Automatic factual question generation from text. Heilman, Michael. Language Technologies Institute School of Computer Science Carnegie Mellon University 2011. paper

  13. Question generation and answering. Linnebank, Floris, Jochem Liem, and Bert Bredeweg. DynaLearn, EC FP7 STREP project,2010. paper

  14. Question generation from paragraphs at UPenn: QGSTEC system description. Mannem, Prashanth, Rashmi Prasad, and Aravind Joshi. QG2010: The Third Workshop on Question Generation,2010. paper

  15. Question generation with minimal recursion semantics. Yao, Xuchen, and Yi Zhang. QG2010: The Third Workshop on Question Generation. 2010. paper

  16. Natural language question generation using syntax and keywords. Kalady S, Elikkottil A, Das R. QG2010: The Third Workshop on Question Generation, 2010. paper

  17. Automatic question generation for literature review writing support. Liu, Ming, Rafael A. Calvo, and Vasile Rus. International Conference on Intelligent Tutoring Systems,2010. paper

  18. Overview of the first question generation shared task evaluation challenge. Rus, Vasile, et al. the Third Workshop on Question Generation, 2010. paper

  19. Question generation in the CODA project. Piwek, Paul, and Svetlana Stoyanchev. no conference, 2010. paper

  20. The first question generation shared task evaluation challenge. Rus V, Wyse B, Piwek P, et al. INLG, 2010. paper

  21. Extracting simplified statements for factual question generation. Heilman, Michael, and Noah A. Smith. QG2010: The Third Workshop on Question Generation, 2010. paper system

  22. Good Question! Statistical Ranking for Question Generation. Heilman, Michael and Smith, Noah A. ACL, 2010.paper dataset1 dataset2

  23. Automation of question generation from sentences. Ali, H., Chali, Y., Hasan, S. A. QG2010: The Third Workshop on Question Generation 2010. paper

  24. Question Generation via Overgenerating Transformations and Ranking. Michael Heilman, Noah A. Smith. CARNEGIE-MELLON UNIV PITTSBURGH PA LANGUAGE TECHNOLOGIES INST, 2009. paper

  25. Automatic question generation and answer judging: a q&a game for language learning. Yushi Xu, Anna Goldie, Stephanie Seneff. SLaTE, 2009. paper

Evaluation

  1. Unifying Human and Statistical Evaluation for Natural Language Generation. Tatsunori B. Hashimoto, Hugh Zhang, Percy Liang. NAACL, 2019. paper code

  2. Evaluating Rewards for Question Generation Models. Hosking T, Riedel S. arXiv, 2019. paper

  3. The price of debiasing automatic metrics in natural language evaluation. Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang arXiv, 2018. paper code

  4. BLEU: a Method for Automatic Evaluation of Machine Translation. Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu. ACL, 2002. paper

  5. Evaluating question answering over linked data. Lopez V, Unger C, Cimiano P, et al. WWW, 2013. paper

  6. The Meteor metric for automatic evaluation of machine translation. Lavie A, Denkowski M J. Machine translation, 2009. paper

  7. Rouge: A package for automatic evaluation of summaries. Lin, Chin-Yew. Text Summarization Branches Out, 2004. paper

Dataset

  1. Program induction by rationale generation: Learning to solve and explain algebraic word problems. Ling W, Yogatama D, Dyer C, et al. arXiv, 2017. paper code

  2. On Generating Characteristic-rich Question Sets for QA Evaluation. Su Y, Sun H, Sadler B, et al. EMNLP, 2016. paper code

  3. Squad: 100,000+ questions for machine comprehension of text. Rajpurkar P, Zhang J, Lopyrev K, et al. arXiv, 2016. paper dataset

  4. Who did what: A large-scale person-centered cloze dataset Onishi T, Wang H, Bansal M, et al. arXiv, 2016. paper dataset

  5. Teaching machines to read and comprehend Hermann K M, Kocisky T, Grefenstette E, et al. NIPS, 2015. paper code

  6. Mctest: A challenge dataset for the open-domain machine comprehension of text. Richardson M, Burges C J C, and Renshaw E. EMNLP, 2013. paper dataset

  7. The Value of Semantic Parse Labeling for Knowledge Base Question Answering. Yih W, Richardson M, Meek C, et al. ACL, 2016. paper dataset

  8. Semantic Parsing on Freebase from Question-Answer Pairs. Berant J, Chou A, Frostig R, et al. EMNLP, 2013. paper

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