{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:28:04Z","timestamp":1783110484447,"version":"3.54.6"},"reference-count":265,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62006218, 61902381, 61773362, and 61872338"],"award-info":[{"award-number":["62006218, 61902381, 61773362, and 61872338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Academy of Artificial Intelligence","award":["BAAI2019ZD0306"],"award-info":[{"award-number":["BAAI2019ZD0306"]}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"crossref","award":["20144310, 2016102, and 2021100"],"award-info":[{"award-number":["20144310, 2016102, and 2021100"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Lenovo-CAS Joint Lab Youth Scientist Project"},{"DOI":"10.13039\/501100012692","name":"K.C. Wong Education Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012692","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission","award":["cstc2017jcyjBX0059"],"award-info":[{"award-number":["cstc2017jcyjBX0059"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.<\/jats:p>","DOI":"10.1145\/3468889","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T15:31:23Z","timestamp":1631115083000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["A Review on Question Generation from Natural Language Text"],"prefix":"10.1145","volume":"40","author":[{"given":"Ruqing","family":"Zhang","sequence":"first","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiafeng","family":"Guo","sequence":"additional","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixing","family":"Fan","sequence":"additional","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Automatic generation of multiple choice questions using dependency-based semantic relations. Soft Comput. 18 (07","author":"Afzal Naveed","year":"2014","unstructured":"Naveed Afzal and Ruslan Mitkov . 2014. Automatic generation of multiple choice questions using dependency-based semantic relations. Soft Comput. 18 (07 2014 ), 1269\u20131281. Naveed Afzal and Ruslan Mitkov. 2014. Automatic generation of multiple choice questions using dependency-based semantic relations. Soft Comput. 18 (07 2014), 1269\u20131281."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 1\u20139.","author":"Agarwal Manish","year":"2011","unstructured":"Manish Agarwal , Rakshit Shah , and Prashanth Mannem . 2011 . Automatic question generation using discourse cues . In Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 1\u20139. Manish Agarwal, Rakshit Shah, and Prashanth Mannem. 2011. Automatic question generation using discourse cues. In Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, 1\u20139."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4405"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the Third Workshop on Question Generation. 58\u201367","author":"Ali Husam","unstructured":"Husam Ali , Yllias Chali , and Sadid A. Hasan . 2010. Automation of question generation from sentences . In Proceedings of the Third Workshop on Question Generation. 58\u201367 . Husam Ali, Yllias Chali, and Sadid A. Hasan. 2010. Automation of question generation from sentences. In Proceedings of the Third Workshop on Question Generation. 58\u201367."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the Actes de la 17e Conference sur le Traitement Automatique des Langues Naturelles Articles courts. 213\u2013218","author":"Ali Husam","unstructured":"Husam Ali , Yllias Chali , and Sadid A. Hasan . 2011. Automation of question generation from sentences . In Proceedings of the Actes de la 17e Conference sur le Traitement Automatique des Langues Naturelles Articles courts. 213\u2013218 . Husam Ali, Yllias Chali, and Sadid A. Hasan. 2011. Automation of question generation from sentences. In Proceedings of the Actes de la 17e Conference sur le Traitement Automatique des Langues Naturelles Articles courts. 213\u2013218."},{"key":"e_1_2_1_6_1","volume-title":"ConvAI3: Generating clarifying questions for open-domain dialogue systems (ClariQ). arXiv preprint arXiv:2009.11352","author":"Aliannejadi Mohammad","year":"2020","unstructured":"Mohammad Aliannejadi , Julia Kiseleva , Aleksandr Chuklin , Jeff Dalton , and Mikhail Burtsev . 2020. ConvAI3: Generating clarifying questions for open-domain dialogue systems (ClariQ). arXiv preprint arXiv:2009.11352 ( 2020 ). Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2020. ConvAI3: Generating clarifying questions for open-domain dialogue systems (ClariQ). arXiv preprint arXiv:2009.11352 (2020)."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 475\u2013484","author":"Aliannejadi Mohammad","unstructured":"Mohammad Aliannejadi , Hamed Zamani , Fabio Crestani , and W. Bruce Croft . 2019. Asking clarifying questions in open-domain information-seeking conversations . In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 475\u2013484 . Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. Bruce Croft. 2019. Asking clarifying questions in open-domain information-seeking conversations. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 475\u2013484."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.21236\/ADA439446"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Jacopo Amidei P. Piwek and A. Willis. 2018. Evaluation methodologies in automatic question generation 2013\u20132018. In INLG.  Jacopo Amidei P. Piwek and A. Willis. 2018. Evaluation methodologies in automatic question generation 2013\u20132018. In INLG.","DOI":"10.18653\/v1\/W18-6537"},{"key":"e_1_2_1_10_1","unstructured":"L. Anderson D. Krathwohl and B. Bloom. 2000. A taxonomy for learning teaching and assessing: A revision of bloom\u2019s taxonomy of educational objectives Longman.  L. Anderson D. Krathwohl and B. Bloom. 2000. A taxonomy for learning teaching and assessing: A revision of bloom\u2019s taxonomy of educational objectives Longman."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0079-7421(08)60269-8"},{"key":"e_1_2_1_12_1","unstructured":"J. Araki Dheeraj Rajagopal S. Sankaranarayanan Susan Holm Yukari Yamakawa and T. Mitamura. 2016. Generating questions and multiple-choice answers using semantic analysis of texts. In COLING.  J. Araki Dheeraj Rajagopal S. Sankaranarayanan Susan Holm Yukari Yamakawa and T. Mitamura. 2016. Generating questions and multiple-choice answers using semantic analysis of texts. In COLING."},{"key":"e_1_2_1_14_1","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR.  Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR."},{"key":"e_1_2_1_15_1","unstructured":"Hangbo Bao Li Dong Furu Wei Wenhui Wang Nan Yang X. Liu Yu Wang Songhao Piao Jianfeng Gao M. Zhou and H. Hon. 2020. UniLMv2: Pseudo-masked language models for unified language model pre-training. ArXiv abs\/2002.12804 (2020).  Hangbo Bao Li Dong Furu Wei Wenhui Wang Nan Yang X. Liu Yu Wang Songhao Piao Jianfeng Gao M. Zhou and H. Hon. 2020. UniLMv2: Pseudo-masked language models for unified language model pre-training. ArXiv abs\/2002.12804 (2020)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2018.2859777"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.sdp-1.4"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the International Workshop on Health Intelligence. Springer, 69\u201381","author":"Bhatia Parminder","unstructured":"Parminder Bhatia , Kristjan Arumae , and B. Celikkaya . 2020. Dynamic transfer learning for named entity recognition . In Proceedings of the International Workshop on Health Intelligence. Springer, 69\u201381 . Parminder Bhatia, Kristjan Arumae, and B. Celikkaya. 2020. Dynamic transfer learning for named entity recognition. In Proceedings of the International Workshop on Health Intelligence. Springer, 69\u201381."},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 3rd Workshop on Question Generation. questiongeneration.org.","author":"Boyer K.","unstructured":"K. Boyer and P. Piwek . 2010 . In Proceedings of the 3rd Workshop on Question Generation. questiongeneration.org. K. Boyer and P. Piwek. 2010. In Proceedings of the 3rd Workshop on Question Generation. questiongeneration.org."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the Conference on Human Information Interaction and Retrieval. 345\u2013348","author":"Braslavski Pavel","year":"2017","unstructured":"Pavel Braslavski , Denis Savenkov , Eugene Agichtein , and Alina Dubatovka . 2017 . What do you mean exactly? Analyzing clarification questions in CQA . In Proceedings of the Conference on Human Information Interaction and Retrieval. 345\u2013348 . Pavel Braslavski, Denis Savenkov, Eugene Agichtein, and Alina Dubatovka. 2017. What do you mean exactly? Analyzing clarification questions in CQA. In Proceedings of the Conference on Human Information Interaction and Retrieval. 345\u2013348."},{"key":"e_1_2_1_21_1","volume-title":"MS MARCO: A human-generated machine reading comprehension dataset. ArXiv abs\/1611.09268","author":"Campos Daniel Fernando","year":"2016","unstructured":"Daniel Fernando Campos , T. Nguyen , M. Rosenberg , Xia Song , Jianfeng Gao , Saurabh Tiwary , Rangan Majumder , L. Deng , and Bhaskar Mitra . 2016 . MS MARCO: A human-generated machine reading comprehension dataset. ArXiv abs\/1611.09268 (2016). Daniel Fernando Campos, T. Nguyen, M. Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, L. Deng, and Bhaskar Mitra. 2016. MS MARCO: A human-generated machine reading comprehension dataset. ArXiv abs\/1611.09268 (2016)."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the Workshop on Widening NLP. Association for Computational Linguistics, 53\u201356","author":"Cao Yang Trista","year":"2019","unstructured":"Yang Trista Cao , Sudha Rao , and Hal Daum\u00e9 III. 2019 . Controlling the specificity of clarification question generation . In Proceedings of the Workshop on Widening NLP. Association for Computational Linguistics, 53\u201356 . Yang Trista Cao, Sudha Rao, and Hal Daum\u00e9 III. 2019. Controlling the specificity of clarification question generation. In Proceedings of the Workshop on Widening NLP. Association for Computational Linguistics, 53\u201356."},{"key":"e_1_2_1_23_1","unstructured":"A. Celikyilmaz E. Clark and J. Gao. 2020. Evaluation of text generation: A survey. arXiv preprint arXiv:2006.14799.  A. Celikyilmaz E. Clark and J. Gao. 2020. Evaluation of text generation: A survey. arXiv preprint arXiv:2006.14799."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2018.2889100"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.21"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-6518"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00206"},{"key":"e_1_2_1_28_1","unstructured":"Ying-Hong Chan and Yao-Chung Fan. 2019. A recurrent BERT-based model for question generation. In MRQA@EMNLP.  Ying-Hong Chan and Yao-Chung Fan. 2019. A recurrent BERT-based model for question generation. In MRQA@EMNLP."},{"key":"e_1_2_1_29_1","volume-title":"Reading Wikipedia to answer open-domain questions. ArXiv abs\/1704.00051","author":"Chen Danqi","year":"2017","unstructured":"Danqi Chen , A. Fisch , J. Weston , and Antoine Bordes . 2017. Reading Wikipedia to answer open-domain questions. ArXiv abs\/1704.00051 ( 2017 ). Danqi Chen, A. Fisch, J. Weston, and Antoine Bordes. 2017. Reading Wikipedia to answer open-domain questions. ArXiv abs\/1704.00051 (2017)."},{"key":"e_1_2_1_30_1","volume-title":"Artificial Intelligence in Education, Seiji Isotani, Eva Mill\u00e1n","author":"Chen Guanliang","unstructured":"Guanliang Chen , Jie Yang , and Dragan Gasevic . 2019. A comparative study on question-worthy sentence selection strategies for educational question generation . In Artificial Intelligence in Education, Seiji Isotani, Eva Mill\u00e1n , Amy Ogan, Peter Hastings, Bruce McLaren, and Rose Luckin (Eds.). Springer International Publishing , Cham , 59\u201370. Guanliang Chen, Jie Yang, and Dragan Gasevic. 2019. A comparative study on question-worthy sentence selection strategies for educational question generation. In Artificial Intelligence in Education, Seiji Isotani, Eva Mill\u00e1n, Amy Ogan, Peter Hastings, Bruce McLaren, and Rose Luckin (Eds.). Springer International Publishing, Cham, 59\u201370."},{"key":"e_1_2_1_31_1","unstructured":"Guanliang Chen Jie Yang C. Hauff and G. Houben. 2018. LearningQ: A large-scale dataset for educational question generation. In ICWSM.  Guanliang Chen Jie Yang C. Hauff and G. Houben. 2018. LearningQ: A large-scale dataset for educational question generation. In ICWSM."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401042"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of AIED Workshop on Question Generation. 17\u201324","author":"Chen Wei","year":"2009","unstructured":"Wei Chen and Gregory Aist . 2009 . Generating questions automatically from informational text . In Proceedings of AIED Workshop on Question Generation. 17\u201324 . Wei Chen and Gregory Aist. 2009. Generating questions automatically from informational text. In Proceedings of AIED Workshop on Question Generation. 17\u201324."},{"key":"e_1_2_1_34_1","volume-title":"Piji Li, Lidong Bing, and Irwin King.","author":"Chen Wang","year":"2019","unstructured":"Wang Chen , Hou Pong Chan , Piji Li, Lidong Bing, and Irwin King. 2019 . An integrated approach for keyphrase generation via exploring the power of retrieval and extraction. In NAACL-HLT. Wang Chen, Hou Pong Chan, Piji Li, Lidong Bing, and Irwin King. 2019. An integrated approach for keyphrase generation via exploring the power of retrieval and extraction. In NAACL-HLT."},{"key":"e_1_2_1_35_1","volume-title":"Zaki","author":"Chen Yu","year":"2019","unstructured":"Yu Chen , Lingfei Wu , and Mohammed J . Zaki . 2019 . Natural question generation with reinforcement learning based graph-to-sequence model. CoRR abs\/1910.08832 (2019). Yu Chen, Lingfei Wu, and Mohammed J. Zaki. 2019. Natural question generation with reinforcement learning based graph-to-sequence model. CoRR abs\/1910.08832 (2019)."},{"key":"e_1_2_1_36_1","volume-title":"Zaki","author":"Chen Yu","year":"2020","unstructured":"Yu Chen , Lingfei Wu , and Mohammed J . Zaki . 2020 . Reinforcement learning based graph-to-sequence model for natural question generation. ArXiv abs\/1908.04942 (2020). Yu Chen, Lingfei Wu, and Mohammed J. Zaki. 2020. Reinforcement learning based graph-to-sequence model for natural question generation. ArXiv abs\/1908.04942 (2020)."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"W. S. Cho Y. Zhang Sudha Rao Chris Brockett and S. Lee. 2019. Generating a common question from multiple documents using multi-source encoder-decoder models. ArXiv abs\/1910.11483 (2019).  W. S. Cho Y. Zhang Sudha Rao Chris Brockett and S. Lee. 2019. Generating a common question from multiple documents using multi-source encoder-decoder models. ArXiv abs\/1910.11483 (2019).","DOI":"10.18653\/v1\/D19-5604"},{"key":"e_1_2_1_39_1","unstructured":"W. S. Cho Y. Zhang Sudha Rao A. \u00c7elikyilmaz Chenyan Xiong Jianfeng Gao M. Wang and B. Dolan. 2019. Contrastive multi-document question generation. arXiv preprint arXiv:1911.03047.  W. S. Cho Y. Zhang Sudha Rao A. \u00c7elikyilmaz Chenyan Xiong Jianfeng Gao M. Wang and B. Dolan. 2019. Contrastive multi-document question generation. arXiv preprint arXiv:1911.03047."},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Eunsol Choi H. He Mohit Iyyer Mark Yatskar W. Yih Yejin Choi Percy Liang and L. Zettlemoyer. 2018. QuAC : Question answering in context. ArXiv abs\/1808.07036 (2018).  Eunsol Choi H. He Mohit Iyyer Mark Yatskar W. Yih Yejin Choi Percy Liang and L. Zettlemoyer. 2018. QuAC : Question answering in context. ArXiv abs\/1808.07036 (2018).","DOI":"10.18653\/v1\/D18-1241"},{"key":"e_1_2_1_41_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8789\u20138797","author":"Choi Yunjey","unstructured":"Yunjey Choi , Min-Je Choi , Munyoung Kim , Jung-Woo Ha , S. Kim , and J. Choo . 2018. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8789\u20138797 . Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, S. Kim, and J. Choo. 2018. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8789\u20138797."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939746"},{"key":"e_1_2_1_43_1","volume-title":"Mendes","author":"Coden Anni","year":"2015","unstructured":"Anni Coden , Daniel Gruhl , Neal Lewis , and Pablo N . Mendes . 2015 . Did you mean A or B? Supporting clarification dialog for entity disambiguation. In SumPre-HSWI@ ESWC. Anni Coden, Daniel Gruhl, Neal Lewis, and Pablo N. Mendes. 2015. Did you mean A or B? Supporting clarification dialog for entity disambiguation. In SumPre-HSWI@ ESWC."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1207\/S15328023TOP2702_01"},{"key":"e_1_2_1_45_1","unstructured":"P. Costa and Ivandr\u00e9 Paraboni. 2019. Personality-dependent neural text summarization. In RANLP.  P. Costa and Ivandr\u00e9 Paraboni. 2019. Personality-dependent neural text summarization. In RANLP."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.5555\/2187741.2187749"},{"key":"e_1_2_1_47_1","volume-title":"Multi-task learning with multi-view attention for answer selection and knowledge base question answering. ArXiv abs\/1812.02354","author":"Deng Yang","year":"2019","unstructured":"Yang Deng , Yuexiang Xie , Y. Li , Min Yang , Nan Du , W. Fan , Kai Lei , and Ying Shen . 2019. Multi-task learning with multi-view attention for answer selection and knowledge base question answering. ArXiv abs\/1812.02354 ( 2019 ). Yang Deng, Yuexiang Xie, Y. Li, Min Yang, Nan Du, W. Fan, Kai Lei, and Ying Shen. 2019. Multi-task learning with multi-view attention for answer selection and knowledge base question answering. ArXiv abs\/1812.02354 (2019)."},{"key":"e_1_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Michael J. Denkowski and A. Lavie. 2014. Meteor universal: Language specific translation evaluation for any target language. In WMT@ACL.  Michael J. Denkowski and A. Lavie. 2014. Meteor universal: Language specific translation evaluation for any target language. In WMT@ACL.","DOI":"10.3115\/v1\/W14-3348"},{"key":"e_1_2_1_49_1","unstructured":"Emily L. Denton Soumith Chintala Arthur Szlam and R. Fergus. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS.  Emily L. Denton Soumith Chintala Arthur Szlam and R. Fergus. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS."},{"key":"e_1_2_1_50_1","volume-title":"BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_2_1_51_1","volume-title":"Manning","author":"Dhole Kaustubh","year":"2020","unstructured":"Kaustubh Dhole and Christopher D . Manning . 2020 . Syn-QG: Syntactic and shallow semantic rules for question generation. In Proceedings of the 58th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics , 752\u2013765. Kaustubh Dhole and Christopher D. Manning. 2020. Syn-QG: Syntactic and shallow semantic rules for question generation. In Proceedings of the 58th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 752\u2013765."},{"key":"e_1_2_1_52_1","unstructured":"Chuong B. Do and A. Ng. 2005. Transfer learning for text classification. In NIPS.  Chuong B. Do and A. Ng. 2005. Transfer learning for text classification. In NIPS."},{"key":"e_1_2_1_53_1","volume-title":"Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc","author":"Dong Li","unstructured":"Li Dong , Nan Yang , Wenhui Wang , Furu Wei , Xiaodong Liu , Yu Wang , Jianfeng Gao , Ming Zhou , and Hsiao-Wuen Hon . 2019. Unified language model pre-training for natural language understanding and generation . In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc , E. Fox, and R. Garnett (Eds.). Curran Associates, Inc. , 13063\u201313075. Retrieved from http:\/\/papers.nips.cc\/paper\/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation.pdf. Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 13063\u201313075. Retrieved from http:\/\/papers.nips.cc\/paper\/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation.pdf."},{"key":"e_1_2_1_54_1","volume-title":"Natural Language Processing and Chinese Computing","author":"Dong Xiaozheng","unstructured":"Xiaozheng Dong , Yu Hong , Xin Chen , Weikang Li , Min Zhang , and Qiaoming Zhu . 2018. Neural question generation with semantics of question type . In Natural Language Processing and Chinese Computing , Min Zhang, Vincent Ng, Dongyan Zhao, Sujian Li, and Hongying Zan (Eds.). Springer International Publishing , Cham , 213\u2013223. Xiaozheng Dong, Yu Hong, Xin Chen, Weikang Li, Min Zhang, and Qiaoming Zhu. 2018. Neural question generation with semantics of question type. In Natural Language Processing and Chinese Computing, Min Zhang, Vincent Ng, Dongyan Zhao, Sujian Li, and Hongying Zan (Eds.). Springer International Publishing, Cham, 213\u2013223."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1219"},{"key":"e_1_2_1_56_1","doi-asserted-by":"crossref","unstructured":"X. Du and Claire Cardie. 2018. Harvesting paragraph-level question-answer pairs from Wikipedia. In ACL.  X. Du and Claire Cardie. 2018. Harvesting paragraph-level question-answer pairs from Wikipedia. In ACL.","DOI":"10.18653\/v1\/P18-1177"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1123"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1090"},{"key":"e_1_2_1_59_1","unstructured":"M. Dunn Levent Sagun M. Higgins V. U. G\u00fcney Volkan Cirik and K. Cho. 2017. SearchQA: A new Q&A dataset augmented with context from a search engine. ArXiv abs\/1704.05179 (2017).  M. Dunn Levent Sagun M. Higgins V. U. G\u00fcney Volkan Cirik and K. Cho. 2017. SearchQA: A new Q&A dataset augmented with context from a search engine. ArXiv abs\/1704.05179 (2017)."},{"key":"e_1_2_1_60_1","volume-title":"FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. arXiv preprint arXiv:2005.03754","author":"Durmus Esin","year":"2020","unstructured":"Esin Durmus , He He , and Mona Diab . 2020 . FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. arXiv preprint arXiv:2005.03754 (2020). Esin Durmus, He He, and Mona Diab. 2020. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. arXiv preprint arXiv:2005.03754 (2020)."},{"key":"e_1_2_1_61_1","volume-title":"Semantic question generation using artificial immunity. I.J. Mod. Educ. Comput. Sci. 7 (01","author":"Eldesoky Ibrahim","year":"2015","unstructured":"Ibrahim Eldesoky . 2015. Semantic question generation using artificial immunity. I.J. Mod. Educ. Comput. Sci. 7 (01 2015 ), 1\u20138. Ibrahim Eldesoky. 2015. Semantic question generation using artificial immunity. I.J. Mod. Educ. Comput. Sci. 7 (01 2015), 1\u20138."},{"key":"e_1_2_1_62_1","doi-asserted-by":"crossref","unstructured":"Hady ElSahar C. Gravier and F. Laforest. 2018. Zero-shot question generation from knowledge graphs for unseen predicates and entity types. ArXiv abs\/1802.06842 (2018).  Hady ElSahar C. Gravier and F. Laforest. 2018. Zero-shot question generation from knowledge graphs for unseen predicates and entity types. ArXiv abs\/1802.06842 (2018).","DOI":"10.18653\/v1\/N18-1020"},{"key":"e_1_2_1_63_1","doi-asserted-by":"crossref","unstructured":"A. R. Fabbri Patrick Ng Zhiguo Wang Ramesh Nallapati and B. Xiang. 2020. Template-based question generation from retrieved sentences for improved unsupervised question answering. In ACL.  A. R. Fabbri Patrick Ng Zhiguo Wang Ramesh Nallapati and B. Xiang. 2020. Template-based question generation from retrieved sentences for improved unsupervised question answering. In ACL.","DOI":"10.18653\/v1\/2020.acl-main.413"},{"key":"e_1_2_1_64_1","unstructured":"Angela Fan David Grangier and M. Auli. 2018. Controllable abstractive summarization. In NMT@ACL.  Angela Fan David Grangier and M. Auli. 2018. Controllable abstractive summarization. In NMT@ACL."},{"key":"e_1_2_1_65_1","unstructured":"Zhihao Fan Zhongyu Wei Piji Li Yanyan Lan and X. Huang. 2018. A question type driven framework to diversify visual question generation. In IJCAI.  Zhihao Fan Zhongyu Wei Piji Li Yanyan Lan and X. Huang. 2018. A question type driven framework to diversify visual question generation. In IJCAI."},{"key":"e_1_2_1_66_1","unstructured":"Zhihao Fan Zhongyu Wei S. Wang Y. Liu and X. Huang. 2018. A reinforcement learning framework for natural question generation using bi-discriminators. In COLING.  Zhihao Fan Zhongyu Wei S. Wang Y. Liu and X. Huang. 2018. A reinforcement learning framework for natural question generation using bi-discriminators. In COLING."},{"key":"e_1_2_1_67_1","volume-title":"Accelerating real-time question answering via question generation. ArXiv abs\/2009.05167","author":"Fang Yuwei","year":"2020","unstructured":"Yuwei Fang , Shuohang Wang , Zhe Gan , S. Sun , and Jing jing Liu . 2020. Accelerating real-time question answering via question generation. ArXiv abs\/2009.05167 ( 2020 ). Yuwei Fang, Shuohang Wang, Zhe Gan, S. Sun, and Jing jing Liu. 2020. Accelerating real-time question answering via question generation. ArXiv abs\/2009.05167 (2020)."},{"key":"e_1_2_1_68_1","doi-asserted-by":"crossref","unstructured":"M. Flor and Brian Riordan. 2018. A semantic role-based approach to open-domain automatic question generation. In BEA@NAACL-HLT.  M. Flor and Brian Riordan. 2018. A semantic role-based approach to open-domain automatic question generation. In BEA@NAACL-HLT.","DOI":"10.18653\/v1\/W18-0530"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-0530"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/690"},{"key":"e_1_2_1_71_1","first-page":"19","volume-title":"Proceedings of the 57th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4853\u20134862","author":"Gao Yifan","year":"1865","unstructured":"Yifan Gao , Piji Li , Irwin King , and Michael R. Lyu . 2019. Interconnected question generation with coreference alignment and conversation flow modeling . In Proceedings of the 57th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4853\u20134862 . DOI:https:\/\/doi.org\/10. 1865 3\/v1\/P 19 - 1480 10.18653\/v1 Yifan Gao, Piji Li, Irwin King, and Michael R. Lyu. 2019. Interconnected question generation with coreference alignment and conversation flow modeling. In Proceedings of the 57th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4853\u20134862. DOI:https:\/\/doi.org\/10.18653\/v1\/P19-1480"},{"key":"e_1_2_1_73_1","doi-asserted-by":"crossref","unstructured":"Han Guo Ramakanth Pasunuru and Mohit Bansal. 2018. Soft layer-specific multi-task summarization with entailment and question generation. In ACL.  Han Guo Ramakanth Pasunuru and Mohit Bansal. 2018. Soft layer-specific multi-task summarization with entailment and question generation. In ACL.","DOI":"10.18653\/v1\/P18-1064"},{"key":"e_1_2_1_74_1","volume-title":"Reinforced multi-task approach for multi-hop question generation. CoRR abs\/2004.02143","author":"Gupta Deepak","year":"2020","unstructured":"Deepak Gupta , Hardik Chauhan , Asif Ekbal , and Pushpak Bhattacharyya . 2020. Reinforced multi-task approach for multi-hop question generation. CoRR abs\/2004.02143 ( 2020 ). Deepak Gupta, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2020. Reinforced multi-task approach for multi-hop question generation. CoRR abs\/2004.02143 (2020)."},{"key":"e_1_2_1_75_1","doi-asserted-by":"crossref","unstructured":"D. Gupta H. Chauhan Asif Ekbal and P. Bhattacharyya. 2020. Reinforced multi-task approach for multi-hop question generation. ArXiv abs\/2004.02143 (2020).  D. Gupta H. Chauhan Asif Ekbal and P. Bhattacharyya. 2020. Reinforced multi-task approach for multi-hop question generation. ArXiv abs\/2004.02143 (2020).","DOI":"10.18653\/v1\/2020.coling-main.249"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9030386"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219866"},{"key":"e_1_2_1_78_1","doi-asserted-by":"crossref","unstructured":"V. Harrison and M. Walker. 2018. Neural generation of diverse questions using answer focus contextual and linguistic features. In INLG.  V. Harrison and M. Walker. 2018. Neural generation of diverse questions using answer focus contextual and linguistic features. In INLG.","DOI":"10.18653\/v1\/W18-6536"},{"key":"e_1_2_1_79_1","volume-title":"Proceedings of the IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE\u201919)","author":"Hatua Amartya","unstructured":"Amartya Hatua , T. T. Nguyen , and A. Sung . 2019. Dialogue generation using self-attention generative adversarial network . In Proceedings of the IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE\u201919) . 33\u201338. Amartya Hatua, T. T. Nguyen, and A. Sung. 2019. Dialogue generation using self-attention generative adversarial network. In Proceedings of the IEEE International Conference on Conversational Data & Knowledge Engineering (CDKE\u201919). 33\u201338."},{"key":"e_1_2_1_80_1","unstructured":"T. He J. Zhang Z. Zhou and J. Glass. 2021. Quantifying exposure bias for neural language generation. arXiv preprint arXiv:1905.10617 (2021).  T. He J. Zhang Z. Zhou and J. Glass. 2021. Quantifying exposure bias for neural language generation. arXiv preprint arXiv:1905.10617 (2021)."},{"key":"e_1_2_1_81_1","doi-asserted-by":"crossref","unstructured":"Wei He Kai Liu Jing Liu Yajuan Lyu Shiqi Zhao X. Xiao Yulong Liu Yizhong Wang H. Wu Qiaoqiao She Xuan Liu T. Wu and H. Wang. 2018. DuReader: A Chinese machine reading comprehension dataset from real-world applications. ArXiv abs\/1711.05073 (2018).  Wei He Kai Liu Jing Liu Yajuan Lyu Shiqi Zhao X. Xiao Yulong Liu Yizhong Wang H. Wu Qiaoqiao She Xuan Liu T. Wu and H. Wang. 2018. DuReader: A Chinese machine reading comprehension dataset from real-world applications. ArXiv abs\/1711.05073 (2018).","DOI":"10.18653\/v1\/W18-2605"},{"key":"e_1_2_1_82_1","volume-title":"Smith","author":"Heilman Michael","year":"2009","unstructured":"Michael Heilman and Noah A . Smith . 2009 . Question Generation via Overgenerating Transformations and Ranking. Technical Report. Carnegie-Mellon Univ Pittsburgh pa language technologies insT. Michael Heilman and Noah A. Smith. 2009. Question Generation via Overgenerating Transformations and Ranking. Technical Report. Carnegie-Mellon Univ Pittsburgh pa language technologies insT."},{"key":"e_1_2_1_83_1","volume-title":"Smith","author":"Heilman Michael","year":"2010","unstructured":"Michael Heilman and Noah A . Smith . 2010 . Extracting simplified statements for factual question generation. Michael Heilman and Noah A. Smith. 2010. Extracting simplified statements for factual question generation."},{"key":"e_1_2_1_84_1","volume-title":"Smith","author":"Heilman Michael","year":"2010","unstructured":"Michael Heilman and Noah A . Smith . 2010 . Good question! Statistical ranking for question generation. In HLT-NAACL. Michael Heilman and Noah A. Smith. 2010. Good question! Statistical ranking for question generation. In HLT-NAACL."},{"key":"e_1_2_1_85_1","unstructured":"Felix Hill Antoine Bordes S. Chopra and J. Weston. 2016. The goldilocks principle: Reading children\u2019s books with explicit memory representations. CoRR abs\/1511.02301 (2016).  Felix Hill Antoine Bordes S. Chopra and J. Weston. 2016. The goldilocks principle: Reading children\u2019s books with explicit memory representations. CoRR abs\/1511.02301 (2016)."},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_87_1","doi-asserted-by":"crossref","unstructured":"T. Hosking and S. Riedel. 2019. Evaluating rewards for question generation models. In NAACL-HLT.  T. Hosking and S. Riedel. 2019. Evaluating rewards for question generation models. In NAACL-HLT.","DOI":"10.18653\/v1\/N19-1237"},{"key":"e_1_2_1_88_1","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR\u201918)","author":"Hu Wenpeng","year":"2018","unstructured":"Wenpeng Hu , Bing Liu , Jinwen Ma , Dongyan Zhao , and Rui Yan . 2018 . Aspect-based question generation . In Proceedings of the 6th International Conference on Learning Representations (ICLR\u201918) . OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=rkRR1ynIf. Wenpeng Hu, Bing Liu, Jinwen Ma, Dongyan Zhao, and Rui Yan. 2018. Aspect-based question generation. In Proceedings of the 6th International Conference on Learning Representations (ICLR\u201918). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=rkRR1ynIf."},{"key":"e_1_2_1_89_1","volume-title":"Workshop track-ICLR 2018 Aspect-based question generation.","author":"Hu Wenpeng","unstructured":"Wenpeng Hu , B. Liu , Jinwen Ma , Dongyan Zhao , and R. Yan . 2018 . Workshop track-ICLR 2018 Aspect-based question generation. Wenpeng Hu, B. Liu, Jinwen Ma, Dongyan Zhao, and R. Yan. 2018. Workshop track-ICLR 2018 Aspect-based question generation."},{"key":"e_1_2_1_90_1","unstructured":"Wenpeng Hu B. Liu R. Yan Dongyan Zhao and Jinwen Ma. 2018. Topic-based question generation. In ICLR.  Wenpeng Hu B. Liu R. Yan Dongyan Zhao and Jinwen Ma. 2018. Topic-based question generation. In ICLR."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324915000455"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_1_93_1","doi-asserted-by":"crossref","unstructured":"Masaru Isonuma Toru Fujino Junichiro Mori Y. Matsuo and I. Sakata. 2017. Extractive summarization using multi-task learning with document classification. In EMNLP.  Masaru Isonuma Toru Fujino Junichiro Mori Y. Matsuo and I. Sakata. 2017. Extractive summarization using multi-task learning with document classification. In EMNLP.","DOI":"10.18653\/v1\/D17-1223"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0084.1990.mp52002003.x"},{"key":"e_1_2_1_95_1","doi-asserted-by":"crossref","unstructured":"Mandar Joshi Eunsol Choi Daniel S. Weld and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In ACL.  Mandar Joshi Eunsol Choi Daniel S. Weld and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In ACL.","DOI":"10.18653\/v1\/P17-1147"},{"key":"e_1_2_1_96_1","unstructured":"Mitesh M. Khapra Dinesh Raghu S. Joshi and Sathish Reddy. 2017. Generating natural language question-answer pairs from a knowledge graph using an RNN-based question generation model. In EACL.  Mitesh M. Khapra Dinesh Raghu S. Joshi and Sathish Reddy. 2017. Generating natural language question-answer pairs from a knowledge graph using an RNN-based question generation model. In EACL."},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210160"},{"key":"e_1_2_1_98_1","unstructured":"Yanghoon Kim Hwanhee Lee Joongbo Shin and K. Jung. 2019. Improving neural question generation using answer separation. ArXiv abs\/1809.02393 (2019).  Yanghoon Kim Hwanhee Lee Joongbo Shin and K. Jung. 2019. Improving neural question generation using answer separation. ArXiv abs\/1809.02393 (2019)."},{"key":"e_1_2_1_99_1","unstructured":"Diederik P. Kingma and M. Welling. 2014. Auto-encoding variational Bayes. CoRR abs\/1312.6114 (2014).  Diederik P. Kingma and M. Welling. 2014. Auto-encoding variational Bayes. CoRR abs\/1312.6114 (2014)."},{"key":"e_1_2_1_100_1","unstructured":"Thomas Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. ArXiv abs\/1609.02907 (2017).  Thomas Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. ArXiv abs\/1609.02907 (2017)."},{"key":"e_1_2_1_101_1","unstructured":"T. Klein and M. Nabi. 2019. Learning to answer by learning to ask: Getting the best of GPT-2 and BERT worlds. ArXiv abs\/1911.02365 (2019).  T. Klein and M. Nabi. 2019. Learning to answer by learning to ask: Getting the best of GPT-2 and BERT worlds. ArXiv abs\/1911.02365 (2019)."},{"key":"e_1_2_1_102_1","volume-title":"Inquisitive question generation for high level text comprehension. ArXiv abs\/2010.01657","author":"Ko Wei-Jen","year":"2020","unstructured":"Wei-Jen Ko , Te-Yuan Chen , Yiyan Huang , Greg Durrett , and Junyi Jessy Li. 2020. Inquisitive question generation for high level text comprehension. ArXiv abs\/2010.01657 ( 2020 ). Wei-Jen Ko, Te-Yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. 2020. Inquisitive question generation for high level text comprehension. ArXiv abs\/2010.01657 (2020)."},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00023"},{"key":"e_1_2_1_104_1","volume-title":"Proceedings of the ACM SIGIR on International Conference on Theory of Information Retrieval. 129\u2013132","author":"Krasakis Antonios Minas","year":"2020","unstructured":"Antonios Minas Krasakis , Mohammad Aliannejadi , Nikos Voskarides , and Evangelos Kanoulas . 2020 . Analysing the effect of clarifying questions on document ranking in conversational search . In Proceedings of the ACM SIGIR on International Conference on Theory of Information Retrieval. 129\u2013132 . Antonios Minas Krasakis, Mohammad Aliannejadi, Nikos Voskarides, and Evangelos Kanoulas. 2020. Analysing the effect of clarifying questions on document ranking in conversational search. In Proceedings of the ACM SIGIR on International Conference on Theory of Information Retrieval. 129\u2013132."},{"key":"e_1_2_1_105_1","volume-title":"Generating question-answer hierarchies. arXiv preprint arXiv:1906.02622","author":"Krishna Kalpesh","year":"2019","unstructured":"Kalpesh Krishna and Mohit Iyyer . 2019. Generating question-answer hierarchies. arXiv preprint arXiv:1906.02622 ( 2019 ). Kalpesh Krishna and Mohit Iyyer. 2019. Generating question-answer hierarchies. arXiv preprint arXiv:1906.02622 (2019)."},{"key":"e_1_2_1_106_1","doi-asserted-by":"crossref","unstructured":"V. Kumar Yuncheng Hua G. Ramakrishnan G. Qi L. Gao and Yuan-Fang Li. 2019. Difficulty-controllable multi-hop question generation from knowledge graphs. In SEMWEB.  V. Kumar Yuncheng Hua G. Ramakrishnan G. Qi L. Gao and Yuan-Fang Li. 2019. Difficulty-controllable multi-hop question generation from knowledge graphs. In SEMWEB.","DOI":"10.1007\/978-3-030-30793-6_22"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30793-6_22"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1481"},{"key":"e_1_2_1_109_1","doi-asserted-by":"crossref","unstructured":"V. Kumar G. Ramakrishnan and Yuan-Fang Li. 2019. Putting the horse before the cart: A generator-evaluator framework for question generation from text. In CoNLL.  V. Kumar G. Ramakrishnan and Yuan-Fang Li. 2019. Putting the horse before the cart: A generator-evaluator framework for question generation from text. In CoNLL.","DOI":"10.18653\/v1\/K19-1076"},{"key":"e_1_2_1_110_1","volume-title":"Proceedings of the ICCE.","author":"Kunichika Hidenobu","year":"2004","unstructured":"Hidenobu Kunichika , Tomoki Katayama , Tsukasa Hirashima , and Akira Takeuchi . 2004 . Automated question generation methods for intelligent English learning systems and its evaluation . In Proceedings of the ICCE. Hidenobu Kunichika, Tomoki Katayama, Tsukasa Hirashima, and Akira Takeuchi. 2004. Automated question generation methods for intelligent English learning systems and its evaluation. In Proceedings of the ICCE."},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-019-00186-y"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-019-00186-y"},{"key":"e_1_2_1_113_1","volume-title":"Weinberger","author":"Kusner Matt J.","year":"2015","unstructured":"Matt J. Kusner , Yu Sun , Nicholas I. Kolkin , and Kilian Q . Weinberger . 2015 . From word embeddings to document distances. In ICML. Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger. 2015. From word embeddings to document distances. In ICML."},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00276"},{"key":"e_1_2_1_115_1","volume-title":"Proceedings of the 58th Meeting of the Association for Computational Linguistics: System Demonstrations. 380\u2013387","author":"Laban Philippe","unstructured":"Philippe Laban , John Canny , and Marti A. Hearst . 2020. What\u2019s the latest? A question-driven news chatbot . In Proceedings of the 58th Meeting of the Association for Computational Linguistics: System Demonstrations. 380\u2013387 . Philippe Laban, John Canny, and Marti A. Hearst. 2020. What\u2019s the latest? A question-driven news chatbot. In Proceedings of the 58th Meeting of the Association for Computational Linguistics: System Demonstrations. 380\u2013387."},{"key":"e_1_2_1_116_1","volume-title":"Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Labutov Igor","unstructured":"Igor Labutov , Sumit Basu , and Lucy Vanderwende . 2015. Deep questions without deep understanding . In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics , 889\u2013898. Igor Labutov, Sumit Basu, and Lucy Vanderwende. 2015. Deep questions without deep understanding. In Proceedings of the 53rd Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 889\u2013898."},{"key":"e_1_2_1_117_1","doi-asserted-by":"crossref","unstructured":"I. Labutov Sumit Basu and Lucy Vanderwende. 2015. Deep questions without deep understanding. In ACL.  I. Labutov Sumit Basu and Lucy Vanderwende. 2015. Deep questions without deep understanding. In ACL.","DOI":"10.3115\/v1\/P15-1086"},{"key":"e_1_2_1_118_1","volume-title":"RACE: Large-scale reading comprehension dataset from examinations. In EMNLP.","author":"Lai Guokun","year":"2017","unstructured":"Guokun Lai , Qizhe Xie , Hanxiao Liu , Y. Yang , and E. Hovy . 2017 . RACE: Large-scale reading comprehension dataset from examinations. In EMNLP. Guokun Lai, Qizhe Xie, Hanxiao Liu, Y. Yang, and E. Hovy. 2017. RACE: Large-scale reading comprehension dataset from examinations. In EMNLP."},{"key":"e_1_2_1_119_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917)","author":"Ledig C.","unstructured":"C. Ledig , L. Theis , Ferenc Husz\u00e1r , J. Caballero , Andrew Aitken , Alykhan Tejani , J. Totz , Zehan Wang , and W. Shi . 2017. Photo-realistic single image super-resolution using a generative adversarial network . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917) . 105\u2013114. C. Ledig, L. Theis, Ferenc Husz\u00e1r, J. Caballero, Andrew Aitken, Alykhan Tejani, J. Totz, Zehan Wang, and W. Shi. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201917). 105\u2013114."},{"key":"e_1_2_1_120_1","volume-title":"Donghwan Kim, and Sung Ju Hwang.","author":"Lee D.","year":"2020","unstructured":"D. Lee , Seanie Lee , Woo Tae Jeong , Donghwan Kim, and Sung Ju Hwang. 2020 . Generating diverse and consistent QA pairs from contexts with information-maximizing hierarchical conditional VAEs. In ACL. D. Lee, Seanie Lee, Woo Tae Jeong, Donghwan Kim, and Sung Ju Hwang. 2020. Generating diverse and consistent QA pairs from contexts with information-maximizing hierarchical conditional VAEs. In ACL."},{"key":"e_1_2_1_121_1","unstructured":"Wen-Yu Lee Y. Kuo Peng-Ju Hsieh Wen-Feng Cheng Ting-Hsuan Chao Hui-Lan Hsieh Chieh-En Tsai Hsiao-Ching Chang Jia-Shin Lan and W. Hsu. 2015. Unsupervised latent aspect discovery for diverse event summarization. In MM\u201915.  Wen-Yu Lee Y. Kuo Peng-Ju Hsieh Wen-Feng Cheng Ting-Hsuan Chao Hui-Lan Hsieh Chieh-En Tsai Hsiao-Ching Chang Jia-Shin Lan and W. Hsu. 2015. Unsupervised latent aspect discovery for diverse event summarization. In MM\u201915."},{"key":"e_1_2_1_122_1","volume-title":"Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC\u201906)","author":"Levy Roger","year":"2006","unstructured":"Roger Levy and Galen Andrew . 2006 . Tregex and Tsurgeon: Tools for querying and manipulating tree data structures . In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC\u201906) . European Language Resources Association (ELRA). Roger Levy and Galen Andrew. 2006. Tregex and Tsurgeon: Tools for querying and manipulating tree data structures. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC\u201906). European Language Resources Association (ELRA)."},{"key":"e_1_2_1_123_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis , Yinhan Liu , Naman Goyal , Marjan Ghazvininejad , Abdelrahman Mohamed , Omer Levy , Ves Stoyanov , and Luke Zettlemoyer . 2019 . Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019). Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)."},{"key":"e_1_2_1_124_1","doi-asserted-by":"crossref","unstructured":"J. Li M. Galley Chris Brockett Jianfeng Gao and W. Dolan. 2016. A diversity-promoting objective function for neural conversation models. In HLT-NAACL.  J. Li M. Galley Chris Brockett Jianfeng Gao and W. Dolan. 2016. A diversity-promoting objective function for neural conversation models. In HLT-NAACL.","DOI":"10.18653\/v1\/N16-1014"},{"key":"e_1_2_1_125_1","volume-title":"Lyu","author":"Li Jingjing","year":"2019","unstructured":"Jingjing Li , Yifan Gao , Lidong Bing , Irwin King , and Michael R . Lyu . 2019 . Improving question generation with to the point context. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics , 3216\u20133226. DOI:https:\/\/doi.org\/10.18653\/v1\/D19-1317 10.18653\/v1 Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, and Michael R. Lyu. 2019. Improving question generation with to the point context. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, 3216\u20133226. DOI:https:\/\/doi.org\/10.18653\/v1\/D19-1317"},{"key":"e_1_2_1_126_1","unstructured":"J. Li Alexander H. Miller S. Chopra Marc\u2019Aurelio Ranzato and J. Weston. 2017. Learning through dialogue interactions by asking questions. In ICLR.  J. Li Alexander H. Miller S. Chopra Marc\u2019Aurelio Ranzato and J. Weston. 2017. Learning through dialogue interactions by asking questions. In ICLR."},{"key":"e_1_2_1_127_1","volume-title":"Deeper insights into graph convolutional networks for semi-supervised learning. ArXiv abs\/1801.07606","author":"Li Qimai","year":"2018","unstructured":"Qimai Li , Zhichao Han , and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. ArXiv abs\/1801.07606 ( 2018 ). Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. ArXiv abs\/1801.07606 (2018)."},{"key":"e_1_2_1_128_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6116\u20136124","author":"Li Yikang","unstructured":"Yikang Li , N. Duan , B. Zhou , X. Chu , Wanli Ouyang , and X. Wang . 2018. Visual question generation as dual task of visual question answering . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6116\u20136124 . Yikang Li, N. Duan, B. Zhou, X. Chu, Wanli Ouyang, and X. Wang. 2018. Visual question generation as dual task of visual question answering. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 6116\u20136124."},{"key":"e_1_2_1_129_1","volume-title":"Question generation through transfer learning","author":"Liao Yin-Hsiang","unstructured":"Yin-Hsiang Liao and Jia-Ling Koh . 2020. Question generation through transfer learning . In IEA\/AIE. Yin-Hsiang Liao and Jia-Ling Koh. 2020. Question generation through transfer learning. In IEA\/AIE."},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815833.2815842"},{"key":"e_1_2_1_131_1","volume-title":"ACL","author":"Lin Chin-Yew","year":"2004","unstructured":"Chin-Yew Lin . 2004 . ROUGE: A package for automatic evaluation of summaries . In ACL 2004. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In ACL 2004."},{"key":"e_1_2_1_132_1","volume-title":"Proceedings of the 14th European Workshop on Natural Language Generation. Association for Computational Linguistics, 105\u2013114","author":"Lindberg David","year":"2013","unstructured":"David Lindberg , Fred Popowich , John Nesbit , and Phil Winne . 2013 . Generating natural language questions to support learning on-line . In Proceedings of the 14th European Workshop on Natural Language Generation. Association for Computational Linguistics, 105\u2013114 . David Lindberg, Fred Popowich, John Nesbit, and Phil Winne. 2013. Generating natural language questions to support learning on-line. In Proceedings of the 14th European Workshop on Natural Language Generation. Association for Computational Linguistics, 105\u2013114."},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3379996"},{"key":"e_1_2_1_134_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380270"},{"key":"e_1_2_1_135_1","volume-title":"Learning to generate questions by learning what not to generate. CoRR abs\/1902.10418","author":"Liu Bang","year":"2019","unstructured":"Bang Liu , Mingjun Zhao , Di Niu , Kunfeng Lai , Yancheng He , Haojie Wei , and Yu Xu. 2019. Learning to generate questions by learning what not to generate. CoRR abs\/1902.10418 ( 2019 ). Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, and Yu Xu. 2019. Learning to generate questions by learning what not to generate. CoRR abs\/1902.10418 (2019)."},{"key":"e_1_2_1_136_1","volume-title":"Proceedings of the World Wide Web Conference. 1106\u20131118","author":"Liu B.","unstructured":"B. Liu , Mingjun Zhao , Di Niu , K. Lai , Yancheng He , Haojie Wei , and Y. Xu2019. Learning to generate questions by learning what not to generate . In Proceedings of the World Wide Web Conference. 1106\u20131118 . B. Liu, Mingjun Zhao, Di Niu, K. Lai, Yancheng He, Haojie Wei, and Y. Xu2019. Learning to generate questions by learning what not to generate. In Proceedings of the World Wide Web Conference. 1106\u20131118."},{"key":"e_1_2_1_137_1","unstructured":"Dayiheng Liu Yeyun Gong Jie Fu Wei Liu Yu Yan B. Shao Daxin Jiang J. Lv and N. Duan. 2020. Diverse controllable and keyphrase-aware: A corpus and method for news multi-headline generation. ArXiv abs\/2004.03875 (2020).  Dayiheng Liu Yeyun Gong Jie Fu Wei Liu Yu Yan B. Shao Daxin Jiang J. Lv and N. Duan. 2020. Diverse controllable and keyphrase-aware: A corpus and method for news multi-headline generation. ArXiv abs\/2004.03875 (2020)."},{"key":"e_1_2_1_138_1","unstructured":"L. Liu Yao Lu Min Yang Q. Qu Jia Zhu and H. Li. 2018. Generative adversarial network for abstractive text summarization. ArXiv abs\/1711.09357 (2018).  L. Liu Yao Lu Min Yang Q. Qu Jia Zhu and H. Li. 2018. Generative adversarial network for abstractive text summarization. ArXiv abs\/1711.09357 (2018)."},{"key":"e_1_2_1_139_1","doi-asserted-by":"crossref","unstructured":"T. Liu Bingzhen Wei B. Chang and Z. Sui. 2017. Large-scale simple question generation by template-based seq2seq learning. In NLPCC.  T. Liu Bingzhen Wei B. Chang and Z. Sui. 2017. Large-scale simple question generation by template-based seq2seq learning. In NLPCC.","DOI":"10.1007\/978-3-319-73618-1_7"},{"key":"e_1_2_1_140_1","doi-asserted-by":"crossref","unstructured":"Xiyao Ma Qile Zhu Yanlin Zhou and Xiaolin Li. 2020. Improving question generation with sentence-level semantic matching and answer position inferring. In Proceedings of the 34th AAAI Conference on Artificial Intelligence the 32nd Innovative Applications of Artificial Intelligence Conference the 10th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press 8464\u20138471. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6366.  Xiyao Ma Qile Zhu Yanlin Zhou and Xiaolin Li. 2020. Improving question generation with sentence-level semantic matching and answer position inferring. In Proceedings of the 34th AAAI Conference on Artificial Intelligence the 32nd Innovative Applications of Artificial Intelligence Conference the 10th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press 8464\u20138471. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6366.","DOI":"10.1609\/aaai.v34i05.6366"},{"key":"e_1_2_1_141_1","volume-title":"Asking complex questions with multi-hop answer-focused reasoning. ArXiv abs\/2009.07402","author":"Ma Xiyao","year":"2020","unstructured":"Xiyao Ma , Qile Zhu , Y. Zhou , Xiaolin Li , and Dapeng Wu. 2020. Asking complex questions with multi-hop answer-focused reasoning. ArXiv abs\/2009.07402 ( 2020 ). Xiyao Ma, Qile Zhu, Y. Zhou, Xiaolin Li, and Dapeng Wu. 2020. Asking complex questions with multi-hop answer-focused reasoning. ArXiv abs\/2009.07402 (2020)."},{"key":"e_1_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4410"},{"key":"e_1_2_1_143_1","volume-title":"Proceedings of the QG2010: The Third Workshop on Question Generation. 84\u201391","author":"Mannem Prashanth","year":"2010","unstructured":"Prashanth Mannem , Rashmi Prasad , and Aravind Joshi . 2010 . Question generation from paragraphs at U Penn: QGSTEC system description . In Proceedings of the QG2010: The Third Workshop on Question Generation. 84\u201391 . Prashanth Mannem, Rashmi Prasad, and Aravind Joshi. 2010. Question generation from paragraphs at U Penn: QGSTEC system description. In Proceedings of the QG2010: The Third Workshop on Question Generation. 84\u201391."},{"key":"e_1_2_1_144_1","volume-title":"Nielsen","author":"Mazidi Karen","year":"2015","unstructured":"Karen Mazidi and Rodney D . Nielsen . 2015 . Leveraging multiple views of text for automatic question generation. In Artificial Intelligence in Education, Cristina Conati, Neil Heffernan, Antonija Mitrovic, and M. Felisa Verdejo (Eds.). Springer International Publishing , Cham, 257\u2013266. Karen Mazidi and Rodney D. Nielsen. 2015. Leveraging multiple views of text for automatic question generation. In Artificial Intelligence in Education, Cristina Conati, Neil Heffernan, Antonija Mitrovic, and M. Felisa Verdejo (Eds.). Springer International Publishing, Cham, 257\u2013266."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-6609"},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-3992.2001.tb00055.x"},{"key":"e_1_2_1_147_1","doi-asserted-by":"publisher","DOI":"10.3115\/1118894.1118897"},{"key":"e_1_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324906004177"},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.3115\/1118894.1118897"},{"key":"e_1_2_1_150_1","unstructured":"N. Mostafazadeh I. Misra J. Devlin M. Mitchell X. He and L. Vanderwende. 2016. Generating natural questions about an image. ArXiv abs\/1603.06059 (2016).  N. Mostafazadeh I. Misra J. Devlin M. Mitchell X. He and L. Vanderwende. 2016. Generating natural questions about an image. ArXiv abs\/1603.06059 (2016)."},{"key":"e_1_2_1_151_1","volume-title":"Generating Instruction Automatically for the Reading Strategy of Self-Questioning","author":"Mostow Jack","unstructured":"Jack Mostow and Wei Chen . 2009. Generating Instruction Automatically for the Reading Strategy of Self-Questioning . IOS Press , NLD , 465\u2013472. Jack Mostow and Wei Chen. 2009. Generating Instruction Automatically for the Reading Strategy of Self-Questioning. IOS Press, NLD, 465\u2013472."},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5809"},{"key":"e_1_2_1_153_1","volume-title":"QURIOUS: Question generation pretraining for text generation. ArXiv abs\/2004.11026","author":"Narayan S.","year":"2020","unstructured":"S. Narayan , Gon\u00e7alo Sim\u00e3es , Ji Ma , Hannah Craighead , and R. McDonald . 2020 . QURIOUS: Question generation pretraining for text generation. ArXiv abs\/2004.11026 (2020). S. Narayan, Gon\u00e7alo Sim\u00e3es, Ji Ma, Hannah Craighead, and R. McDonald. 2020. QURIOUS: Question generation pretraining for text generation. ArXiv abs\/2004.11026 (2020)."},{"key":"e_1_2_1_154_1","volume-title":"Khapra","author":"Nema Preksha","year":"2018","unstructured":"Preksha Nema and Mitesh M . Khapra . 2018 . Towards a better metric for evaluating question generation systems. ArXiv abs\/1808.10192 (2018). Preksha Nema and Mitesh M. Khapra. 2018. Towards a better metric for evaluating question generation systems. ArXiv abs\/1808.10192 (2018)."},{"key":"e_1_2_1_155_1","doi-asserted-by":"crossref","unstructured":"Preksha Nema Mitesh M. Khapra Anirban Laha and B. Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In ACL.  Preksha Nema Mitesh M. Khapra Anirban Laha and B. Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In ACL.","DOI":"10.18653\/v1\/P17-1098"},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1326"},{"key":"e_1_2_1_157_1","volume-title":"From doc2query to docTTTTTquery. Online Preprint","author":"Nogueira Rodrigo","year":"2019","unstructured":"Rodrigo Nogueira , Jimmy Lin , and AI Epistemic . 2019. From doc2query to docTTTTTquery. Online Preprint ( 2019 ). Rodrigo Nogueira, Jimmy Lin, and AI Epistemic. 2019. From doc2query to docTTTTTquery. Online Preprint (2019)."},{"key":"e_1_2_1_158_1","unstructured":"Mohammad Norouzi S. Bengio Z. Chen Navdeep Jaitly Mike Schuster Y. Wu and Dale Schuurmans. 2016. Reward augmented maximum likelihood for neural structured prediction. In NIPS.  Mohammad Norouzi S. Bengio Z. Chen Navdeep Jaitly Mike Schuster Y. Wu and Dale Schuurmans. 2016. Reward augmented maximum likelihood for neural structured prediction. In NIPS."},{"key":"e_1_2_1_159_1","volume-title":"Proceedings of the IEEE 9th International Conference on Semantic Computing (IEEE ICSC\u201915)","author":"Odilinye L.","unstructured":"L. Odilinye , F. Popowich , E. Zhang , J. Nesbit , and P. H. Winne . 2015. Aligning automatically generated questions to instructor goals and learner behaviour . In Proceedings of the IEEE 9th International Conference on Semantic Computing (IEEE ICSC\u201915) . 216\u2013223. L. Odilinye, F. Popowich, E. Zhang, J. Nesbit, and P. H. Winne. 2015. Aligning automatically generated questions to instructor goals and learner behaviour. In Proceedings of the IEEE 9th International Conference on Semantic Computing (IEEE ICSC\u201915). 216\u2013223."},{"key":"e_1_2_1_160_1","doi-asserted-by":"publisher","DOI":"10.5087\/dad.2012.204"},{"key":"e_1_2_1_161_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1203"},{"key":"e_1_2_1_162_1","volume-title":"Recent advances in neural question generation. ArXiv abs\/1905.08949","author":"Pan Liangming","year":"2019","unstructured":"Liangming Pan , Wenqiang Lei , Tat-Seng Chua , and Min-Yen Kan . 2019. Recent advances in neural question generation. ArXiv abs\/1905.08949 ( 2019 ). Liangming Pan, Wenqiang Lei, Tat-Seng Chua, and Min-Yen Kan. 2019. Recent advances in neural question generation. ArXiv abs\/1905.08949 (2019)."},{"key":"e_1_2_1_163_1","volume-title":"Proceedings of the 58th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1463\u20131475","author":"Pan Liangming","year":"2020","unstructured":"Liangming Pan , Yuxi Xie , Yansong Feng , Tat-Seng Chua , and Min-Yen Kan . 2020 . Semantic graphs for generating deep questions . In Proceedings of the 58th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1463\u20131475 . DOI:https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.135 10.18653\/v1 Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, and Min-Yen Kan. 2020. Semantic graphs for generating deep questions. In Proceedings of the 58th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1463\u20131475. DOI:https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.135"},{"key":"e_1_2_1_164_1","unstructured":"Liangming Pan Yuxi Xie Yansong Feng Tat-Seng Chua and Min-Yen Kan. 2020. Semantic graphs for generating deep questions. In ACL.  Liangming Pan Yuxi Xie Yansong Feng Tat-Seng Chua and Min-Yen Kan. 2020. Semantic graphs for generating deep questions. In ACL."},{"key":"e_1_2_1_165_1","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201920)","author":"Pan Youcheng","unstructured":"Youcheng Pan , Baotian Hu , Qingcai Chen , Yang Xiang , and X. Wang . 2020. Learning to generate diverse questions from keywords . In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201920) . 8224\u20138228. Youcheng Pan, Baotian Hu, Qingcai Chen, Yang Xiang, and X. Wang. 2020. Learning to generate diverse questions from keywords. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201920). 8224\u20138228."},{"key":"e_1_2_1_166_1","volume-title":"Bleu: A method for automatic evaluation of machine translation. In ACL.","author":"Papineni Kishore","year":"2002","unstructured":"Kishore Papineni , S. Roukos , T. Ward , and Wei-Jing Zhu . 2002 . Bleu: A method for automatic evaluation of machine translation. In ACL. Kishore Papineni, S. Roukos, T. Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In ACL."},{"key":"e_1_2_1_167_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018).  Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_2_1_168_1","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford , Jeffrey Wu , Rewon Child , David Luan , Dario Amodei , and Ilya Sutskever . 2019 . Language models are unsupervised multitask learners . OpenAI Blog 1 , 8 (2019), 9 . Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.1145\/3020165.3020183"},{"key":"e_1_2_1_170_1","volume-title":"Liu","author":"Raffel Colin","year":"2019","unstructured":"Colin Raffel , Noam Shazeer , Adam Roberts , Katherine Lee , Sharan Narang , Michael Matena , Yanqi Zhou , Wei Li , and Peter J . Liu . 2019 . Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019). Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)."},{"key":"e_1_2_1_171_1","volume-title":"Know what you don\u2019t know: Unanswerable questions for SQuAD. ArXiv abs\/1806.03822","author":"Rajpurkar Pranav","year":"2018","unstructured":"Pranav Rajpurkar , Robin Jia , and Percy Liang . 2018. Know what you don\u2019t know: Unanswerable questions for SQuAD. ArXiv abs\/1806.03822 ( 2018 ). Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don\u2019t know: Unanswerable questions for SQuAD. ArXiv abs\/1806.03822 (2018)."},{"key":"e_1_2_1_172_1","volume-title":"100, 000+ questions for machine comprehension of text. ArXiv abs\/1606.05250","author":"Rajpurkar Pranav","year":"2016","unstructured":"Pranav Rajpurkar , Jian Zhang , Konstantin Lopyrev , and Percy Liang . 2016. SQuAD : 100, 000+ questions for machine comprehension of text. ArXiv abs\/1606.05250 ( 2016 ). Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100, 000+ questions for machine comprehension of text. ArXiv abs\/1606.05250 (2016)."},{"key":"e_1_2_1_173_1","first-page":"1","article-title":"Literature review of automatic question generation systems","volume":"5","author":"Rakangor Sheetal","year":"2015","unstructured":"Sheetal Rakangor and Dr. Y. R. Ghodasara . 2015 . Literature review of automatic question generation systems . International Journal of Scientific and Research Publications 5 , 1 (2015), 1 \u2013 5 . Sheetal Rakangor and Dr. Y. R. Ghodasara. 2015. Literature review of automatic question generation systems. International Journal of Scientific and Research Publications 5, 1 (2015), 1\u20135.","journal-title":"International Journal of Scientific and Research Publications"},{"key":"e_1_2_1_174_1","unstructured":"Sudha Rao and Hal Daum\u00e9. 2019. Answer-based adversarial training for generating clarification questions. In NAACL-HLT.  Sudha Rao and Hal Daum\u00e9. 2019. Answer-based adversarial training for generating clarification questions. In NAACL-HLT."},{"key":"e_1_2_1_175_1","volume-title":"Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. arXiv preprint arXiv:1805.04655","author":"Rao Sudha","year":"2018","unstructured":"Sudha Rao and Hal Daum\u00e9 III. 2018. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. arXiv preprint arXiv:1805.04655 ( 2018 ). Sudha Rao and Hal Daum\u00e9 III. 2018. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. arXiv preprint arXiv:1805.04655 (2018)."},{"key":"e_1_2_1_176_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00266"},{"key":"e_1_2_1_177_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/E17-1036"},{"key":"e_1_2_1_178_1","volume-title":"Learning to ask appropriate questions in conversational recommendation. arXiv preprint arXiv:2105.04774","author":"Ren Xuhui","year":"2021","unstructured":"Xuhui Ren , Hongzhi Yin , Tong Chen , Hao Wang , Zi Huang , and Kai Zheng . 2021. Learning to ask appropriate questions in conversational recommendation. arXiv preprint arXiv:2105.04774 ( 2021 ). Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, and Kai Zheng. 2021. Learning to ask appropriate questions in conversational recommendation. arXiv preprint arXiv:2105.04774 (2021)."},{"key":"e_1_2_1_179_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394592"},{"key":"e_1_2_1_180_1","unstructured":"D. Rezende S. Mohamed and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In ICML.  D. Rezende S. Mohamed and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In ICML."},{"key":"e_1_2_1_181_1","unstructured":"M. Richardson C. Burges and Erin Renshaw. 2013. MCTest: A challenge dataset for the open-domain machine comprehension of text. In EMNLP.  M. Richardson C. Burges and Erin Renshaw. 2013. MCTest: A challenge dataset for the open-domain machine comprehension of text. In EMNLP."},{"key":"e_1_2_1_182_1","volume-title":"QGASP: A framework for question generation based on different levels of linguistic information. In INLG.","author":"Rodrigues H.","year":"2016","unstructured":"H. Rodrigues , Lu\u00edsa Coheur , and Eric Nyberg . 2016 . QGASP: A framework for question generation based on different levels of linguistic information. In INLG. H. Rodrigues, Lu\u00edsa Coheur, and Eric Nyberg. 2016. QGASP: A framework for question generation based on different levels of linguistic information. In INLG."},{"key":"e_1_2_1_183_1","volume-title":"AnswerQuest: A system for generating question-answer items from multi-paragraph documents. arXiv preprint arXiv:2103.03820","author":"Roemmele Melissa","year":"2021","unstructured":"Melissa Roemmele , Deep Sidhpura , Steve DeNeefe , and Ling Tsou . 2021. AnswerQuest: A system for generating question-answer items from multi-paragraph documents. arXiv preprint arXiv:2103.03820 ( 2021 ). Melissa Roemmele, Deep Sidhpura, Steve DeNeefe, and Ling Tsou. 2021. AnswerQuest: A system for generating question-answer items from multi-paragraph documents. arXiv preprint arXiv:2103.03820 (2021)."},{"key":"e_1_2_1_184_1","doi-asserted-by":"crossref","unstructured":"Michael Roth and K. Woodsend. 2014. Composition of word representations improves semantic role labelling. In EMNLP.  Michael Roth and K. Woodsend. 2014. Composition of word representations improves semantic role labelling. In EMNLP.","DOI":"10.3115\/v1\/D14-1045"},{"key":"e_1_2_1_185_1","volume-title":"Proceedings of the Workshop Report.","author":"Rus Vasile","year":"2009","unstructured":"Vasile Rus and Graesser Art . 2009 . The question generation task and evaluation challenge . In Proceedings of the Workshop Report. Vasile Rus and Graesser Art. 2009. The question generation task and evaluation challenge. In Proceedings of the Workshop Report."},{"key":"e_1_2_1_186_1","doi-asserted-by":"crossref","unstructured":"V. Rus Z. Cai and A. Graesser. 2007. Experiments on generating questions about facts. In CICLing.  V. Rus Z. Cai and A. Graesser. 2007. Experiments on generating questions about facts. In CICLing.","DOI":"10.1007\/978-3-540-70939-8_39"},{"key":"e_1_2_1_187_1","volume-title":"The 2nd workshop on question generation. In AIED.","author":"Rus V.","unstructured":"V. Rus and J. Lester . 2009 . The 2nd workshop on question generation. In AIED. V. Rus and J. Lester. 2009. The 2nd workshop on question generation. In AIED."},{"key":"e_1_2_1_188_1","doi-asserted-by":"publisher","DOI":"10.5555\/1873738.1873777"},{"key":"e_1_2_1_189_1","doi-asserted-by":"publisher","DOI":"10.5087\/dad.2012.208"},{"key":"e_1_2_1_190_1","doi-asserted-by":"crossref","unstructured":"Mrinmaya Sachan and E. Xing. 2018. Self-training for jointly learning to ask and answer questions. In NAACL-HLT.  Mrinmaya Sachan and E. Xing. 2018. Self-training for jointly learning to ask and answer questions. In NAACL-HLT.","DOI":"10.18653\/v1\/N18-1058"},{"key":"e_1_2_1_191_1","first-page":"167","article-title":"Learning concepts by asking questions","volume":"2","author":"Sammut Claude","year":"1986","unstructured":"Claude Sammut and Ranan B. Banerji . 1986 . Learning concepts by asking questions . Mach. Learn.: Artif. Intell. Appr. 2 (1986), 167 \u2013 192 . Claude Sammut and Ranan B. Banerji. 1986. Learning concepts by asking questions. Mach. Learn.: Artif. Intell. Appr. 2 (1986), 167\u2013192.","journal-title":"Mach. Learn.: Artif. Intell. Appr."},{"key":"e_1_2_1_192_1","volume-title":"Sang and Fien De Meulder","author":"Erik","year":"2003","unstructured":"Erik F. Sang and Fien De Meulder . 2003 . Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. arXiv preprint cs\/0306050 (2003). Erik F. Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. arXiv preprint cs\/0306050 (2003)."},{"key":"e_1_2_1_193_1","volume-title":"Evaluating for diversity in question generation over text. ArXiv abs\/2008.07291","author":"Schlichtkrull M.","year":"2020","unstructured":"M. Schlichtkrull and Weiwei Cheng . 2020. Evaluating for diversity in question generation over text. ArXiv abs\/2008.07291 ( 2020 ). M. Schlichtkrull and Weiwei Cheng. 2020. Evaluating for diversity in question generation over text. ArXiv abs\/2008.07291 (2020)."},{"key":"e_1_2_1_194_1","doi-asserted-by":"crossref","unstructured":"M. Schlichtkrull Thomas Kipf P. Bloem R. V. Berg Ivan Titov and M. Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC.  M. Schlichtkrull Thomas Kipf P. Bloem R. V. Berg Ivan Titov and M. Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_2_1_195_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1604"},{"key":"e_1_2_1_196_1","doi-asserted-by":"crossref","unstructured":"I. Serban Alberto Garc\u00eda-Dur\u00e1n \u00c7aglar G\u00fcl\u00e7ehre Sungjin Ahn A. Chandar A. Courville and Y. Bengio. 2016. Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. ArXiv abs\/1603.06807 (2016).  I. Serban Alberto Garc\u00eda-Dur\u00e1n \u00c7aglar G\u00fcl\u00e7ehre Sungjin Ahn A. Chandar A. Courville and Y. Bengio. 2016. Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. ArXiv abs\/1603.06807 (2016).","DOI":"10.18653\/v1\/P16-1056"},{"key":"e_1_2_1_197_1","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence.","author":"Serban Iulian Vlad","year":"2017","unstructured":"Iulian Vlad Serban , Alessandro Sordoni , Ryan Lowe , Laurent Charlin , Joelle Pineau , Aaron Courville , and Yoshua Bengio . 2017 . A hierarchical latent variable encoder-decoder model for generating dialogues . In Proceedings of the 31st AAAI Conference on Artificial Intelligence. Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In Proceedings of the 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_198_1","volume-title":"Variational question-answer pair generation for machine reading comprehension. ArXiv abs\/2004.03238","author":"Shinoda Kazutoshi","year":"2020","unstructured":"Kazutoshi Shinoda and Akiko Aizawa . 2020. Variational question-answer pair generation for machine reading comprehension. ArXiv abs\/2004.03238 ( 2020 ). Kazutoshi Shinoda and Akiko Aizawa. 2020. Variational question-answer pair generation for machine reading comprehension. ArXiv abs\/2004.03238 (2020)."},{"key":"e_1_2_1_199_1","volume-title":"Automatic Factual Question Generation from Text","author":"Heilman Michael","unstructured":"Michael Heilman . 2011. Automatic Factual Question Generation from Text . Carnegie Mellon University . Michael Heilman. 2011. Automatic Factual Question Generation from Text. Carnegie Mellon University."},{"key":"e_1_2_1_200_1","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1631\u20131642","author":"Socher Richard","year":"2013","unstructured":"Richard Socher , Alex Perelygin , Jean Wu , Jason Chuang , Christopher D. Manning , Andrew Y. Ng , and Christopher Potts . 2013 . Recursive deep models for semantic compositionality over a sentiment treebank . In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1631\u20131642 . Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1631\u20131642."},{"key":"e_1_2_1_201_1","unstructured":"Kihyuk Sohn H. Lee and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In NIPS.  Kihyuk Sohn H. Lee and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In NIPS."},{"key":"e_1_2_1_202_1","volume-title":"A unified query-based generative model for question generation and question answering. CoRR abs\/1709.01058","author":"Song Linfeng","year":"2017","unstructured":"Linfeng Song , Zhiguo Wang , and Wael Hamza . 2017. A unified query-based generative model for question generation and question answering. CoRR abs\/1709.01058 ( 2017 ). Linfeng Song, Zhiguo Wang, and Wael Hamza. 2017. A unified query-based generative model for question generation and question answering. CoRR abs\/1709.01058 (2017)."},{"key":"e_1_2_1_203_1","doi-asserted-by":"crossref","unstructured":"Linfeng Song Z. Wang W. Hamza Yue Zhang and Daniel Gildea. 2018. Leveraging context information for natural question generation. In NAACL-HLT.  Linfeng Song Z. Wang W. Hamza Yue Zhang and Daniel Gildea. 2018. Leveraging context information for natural question generation. In NAACL-HLT.","DOI":"10.18653\/v1\/N18-2090"},{"key":"e_1_2_1_204_1","volume-title":"Domain-specific question generation from a knowledge base. ArXiv abs\/1610.03807","author":"Song Linfeng","year":"2016","unstructured":"Linfeng Song and Lin Zhao . 2016. Domain-specific question generation from a knowledge base. ArXiv abs\/1610.03807 ( 2016 ). Linfeng Song and Lin Zhao. 2016. Domain-specific question generation from a knowledge base. ArXiv abs\/1610.03807 (2016)."},{"key":"e_1_2_1_205_1","unstructured":"Asa Cooper Stickland and I. Murray. 2019. BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning. In ICML.  Asa Cooper Stickland and I. Murray. 2019. BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning. In ICML."},{"key":"e_1_2_1_206_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(99)00037-5"},{"key":"e_1_2_1_207_1","volume-title":"Peng Xu, H. Kim, Zihan Liu, and Pascale Fung.","author":"Su Dan","year":"2019","unstructured":"Dan Su , Yan Xu , Genta Indra Winata , Peng Xu, H. Kim, Zihan Liu, and Pascale Fung. 2019 . Generalizing question answering system with pre-trained language model fine-tuning. In MRQA @EMNLP. Dan Su, Yan Xu, Genta Indra Winata, Peng Xu, H. Kim, Zihan Liu, and Pascale Fung. 2019. Generalizing question answering system with pre-trained language model fine-tuning. In MRQA@EMNLP."},{"key":"e_1_2_1_208_1","volume-title":"Neural models for key phrase detection and question generation. CoRR abs\/1706.04560","author":"Subramanian Sandeep","year":"2017","unstructured":"Sandeep Subramanian , Tong Wang , Xingdi Yuan , and Adam Trischler . 2017. Neural models for key phrase detection and question generation. CoRR abs\/1706.04560 ( 2017 ). Sandeep Subramanian, Tong Wang, Xingdi Yuan, and Adam Trischler. 2017. Neural models for key phrase detection and question generation. CoRR abs\/1706.04560 (2017)."},{"key":"e_1_2_1_209_1","volume-title":"Ram\u00f3n Fern\u00e1ndez Astudillo, and V. Castelli","author":"Sultan Md Arafat","year":"2020","unstructured":"Md Arafat Sultan , Shubham Chandel , Ram\u00f3n Fern\u00e1ndez Astudillo, and V. Castelli . 2020 . On the importance of diversity in question generation for QA. In ACL. Md Arafat Sultan, Shubham Chandel, Ram\u00f3n Fern\u00e1ndez Astudillo, and V. Castelli. 2020. On the importance of diversity in question generation for QA. In ACL."},{"key":"e_1_2_1_210_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1427"},{"key":"e_1_2_1_211_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210002"},{"key":"e_1_2_1_212_1","volume-title":"Question answering and question generation as dual tasks. CoRR abs\/1706.02027","author":"Tang Duyu","year":"2017","unstructured":"Duyu Tang , Nan Duan , Tao Qin , and Ming Zhou . 2017. Question answering and question generation as dual tasks. CoRR abs\/1706.02027 ( 2017 ). Duyu Tang, Nan Duan, Tao Qin, and Ming Zhou. 2017. Question answering and question generation as dual tasks. CoRR abs\/1706.02027 (2017)."},{"key":"e_1_2_1_213_1","unstructured":"Duyu Tang Nan Duan T. Qin and M. Zhou. 2017. Question answering and question generation as dual tasks. ArXiv abs\/1706.02027 (2017).  Duyu Tang Nan Duan T. Qin and M. Zhou. 2017. Question answering and question generation as dual tasks. ArXiv abs\/1706.02027 (2017)."},{"key":"e_1_2_1_214_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1141"},{"key":"e_1_2_1_215_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3418513"},{"key":"e_1_2_1_216_1","doi-asserted-by":"crossref","unstructured":"Adam Trischler T. Wang Xingdi Yuan J. Harris Alessandro Sordoni Philip Bachman and Kaheer Suleman. 2017. NewsQA: A machine comprehension dataset. In Rep4NLP@ACL.  Adam Trischler T. Wang Xingdi Yuan J. Harris Alessandro Sordoni Philip Bachman and Kaheer Suleman. 2017. NewsQA: A machine comprehension dataset. In Rep4NLP@ACL.","DOI":"10.18653\/v1\/W17-2623"},{"key":"e_1_2_1_217_1","doi-asserted-by":"crossref","unstructured":"Luu Anh Tuan Darsh J. Shah and Regina Barzilay. 2020. Capturing greater context for question generation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence the 32nd Innovative Applications of Artificial Intelligence Conference the 10th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press 9065\u20139072. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6440.  Luu Anh Tuan Darsh J. Shah and Regina Barzilay. 2020. Capturing greater context for question generation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence the 32nd Innovative Applications of Artificial Intelligence Conference the 10th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press 9065\u20139072. Retrieved from https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6440.","DOI":"10.1609\/aaai.v34i05.6440"},{"key":"e_1_2_1_218_1","volume-title":"AAAI Workshop. 22\u201326","author":"Ureel Leo C.","unstructured":"Leo C. Ureel , Kenneth D. Forbus , Christopher K. Riesbeck , and Lawrence A. Birnbaum . 2005. Question generation for learning by reading . In AAAI Workshop. 22\u201326 . Leo C. Ureel, Kenneth D. Forbus, Christopher K. Riesbeck, and Lawrence A. Birnbaum. 2005. Question generation for learning by reading. In AAAI Workshop. 22\u201326."},{"key":"e_1_2_1_219_1","doi-asserted-by":"crossref","unstructured":"Stalin Varanasi Saadullah Amin and G. Neumann. 2020. CopyBERT: A unified approach to question generation with self-attention. In NLP4CONVAI.  Stalin Varanasi Saadullah Amin and G. Neumann. 2020. CopyBERT: A unified approach to question generation with self-attention. In NLP4CONVAI.","DOI":"10.18653\/v1\/2020.nlp4convai-1.3"},{"key":"e_1_2_1_220_1","volume-title":"WLV: A question generation system for the QGSTEC 2010 task B.","author":"Varga A.","year":"2010","unstructured":"A. Varga . 2010 . WLV: A question generation system for the QGSTEC 2010 task B. A. Varga. 2010. WLV: A question generation system for the QGSTEC 2010 task B."},{"key":"e_1_2_1_221_1","unstructured":"Cai Zhiqiang Vasile Rus and Graesser Art. 2008. Question generation: Example of a multi-year evaluation campaign. In WS on the QGSTEC.  Cai Zhiqiang Vasile Rus and Graesser Art. 2008. Question generation: Example of a multi-year evaluation campaign. In WS on the QGSTEC."},{"key":"e_1_2_1_222_1","volume-title":"Attention is all you need. ArXiv abs\/1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N. Gomez , L. Kaiser , and Illia Polosukhin . 2017. Attention is all you need. ArXiv abs\/1706.03762 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, L. Kaiser, and Illia Polosukhin. 2017. Attention is all you need. ArXiv abs\/1706.03762 (2017)."},{"key":"e_1_2_1_223_1","volume-title":"Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv:2004.04228","author":"Wang Alex","year":"2020","unstructured":"Alex Wang , Kyunghyun Cho , and Mike Lewis . 2020. Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv:2004.04228 ( 2020 ). Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv:2004.04228 (2020)."},{"key":"e_1_2_1_224_1","volume-title":"Ting Tao, Qi Zhang, and Jingfang Xu.","author":"Wang Bingning","year":"2020","unstructured":"Bingning Wang , Xiao chuan Wang , Ting Tao, Qi Zhang, and Jingfang Xu. 2020 . Neural question generation with answer pivot. In AAAI. Bingning Wang, Xiao chuan Wang, Ting Tao, Qi Zhang, and Jingfang Xu. 2020. Neural question generation with answer pivot. In AAAI."},{"key":"e_1_2_1_225_1","volume-title":"Adversarial domain adaptation for machine reading comprehension. ArXiv abs\/1908.09209","author":"Wang Huazheng","year":"2019","unstructured":"Huazheng Wang , Zhe Gan , Xiaodong Liu , Jingjing Liu , Jianfeng Gao , and Hongning Wang . 2019. Adversarial domain adaptation for machine reading comprehension. ArXiv abs\/1908.09209 ( 2019 ). Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, and Hongning Wang. 2019. Adversarial domain adaptation for machine reading comprehension. ArXiv abs\/1908.09209 (2019)."},{"key":"e_1_2_1_226_1","doi-asserted-by":"crossref","unstructured":"Siyuan Wang Zhongyu Wei Zhihao Fan Yang Liu and Xuanjing Huang. A multi-agent communication framework for question-worthy phrase extraction and question generation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence the 31st Innovative Applications of Artificial Intelligence Conference the 9th AAAI Symposium on Educational Advances in Artificial Intelligence. 7168\u20137175.  Siyuan Wang Zhongyu Wei Zhihao Fan Yang Liu and Xuanjing Huang. A multi-agent communication framework for question-worthy phrase extraction and question generation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence the 31st Innovative Applications of Artificial Intelligence Conference the 9th AAAI Symposium on Educational Advances in Artificial Intelligence. 7168\u20137175.","DOI":"10.1609\/aaai.v33i01.33017168"},{"key":"e_1_2_1_227_1","volume-title":"A joint model for question answering and question generation. ArXiv abs\/1706.01450","author":"Wang T.","year":"2017","unstructured":"T. Wang , Xingdi Yuan , and Adam Trischler . 2017. A joint model for question answering and question generation. ArXiv abs\/1706.01450 ( 2017 ). T. Wang, Xingdi Yuan, and Adam Trischler. 2017. A joint model for question answering and question generation. ArXiv abs\/1706.01450 (2017)."},{"key":"e_1_2_1_228_1","doi-asserted-by":"crossref","unstructured":"W. Wang Shi Feng D. Wang and Y. Zhang. 2019. Answer-guided and semantic coherent question generation in open-domain conversation. In EMNLP\/IJCNLP.  W. Wang Shi Feng D. Wang and Y. Zhang. 2019. Answer-guided and semantic coherent question generation in open-domain conversation. In EMNLP\/IJCNLP.","DOI":"10.18653\/v1\/D19-1511"},{"key":"e_1_2_1_229_1","unstructured":"Wenhui Wang Furu Wei Li Dong Hangbo Bao Nan Yang and M. Zhou. 2020. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. ArXiv abs\/2002.10957 (2020).  Wenhui Wang Furu Wei Li Dong Hangbo Bao Nan Yang and M. Zhou. 2020. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. ArXiv abs\/2002.10957 (2020)."},{"key":"e_1_2_1_230_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1204"},{"key":"e_1_2_1_231_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/528"},{"key":"e_1_2_1_232_1","volume-title":"Controlling the risk of conversational search via reinforcement learning. arXiv preprint arXiv:2101.06327","author":"Wang Zhenduo","year":"2021","unstructured":"Zhenduo Wang and Qingyao Ai. 2021. Controlling the risk of conversational search via reinforcement learning. arXiv preprint arXiv:2101.06327 ( 2021 ). Zhenduo Wang and Qingyao Ai. 2021. Controlling the risk of conversational search via reinforcement learning. arXiv preprint arXiv:2101.06327 (2021)."},{"key":"e_1_2_1_233_1","doi-asserted-by":"publisher","DOI":"10.1145\/3231644.3231654"},{"key":"e_1_2_1_234_1","volume-title":"Automatic question generation from text\u2014An aid to independent study","author":"Wolfe John H.","unstructured":"John H. Wolfe . 1976. Automatic question generation from text\u2014An aid to independent study . In SIGCSE. Association for Computing Machinery , 104\u2013112. John H. Wolfe. 1976. Automatic question generation from text\u2014An aid to independent study. In SIGCSE. Association for Computing Machinery, 104\u2013112."},{"key":"e_1_2_1_235_1","volume-title":"A question type driven and copy loss enhanced framework for answer-agnostic neural question generation. ArXiv abs\/2005.11665","author":"Wu Xiuyu","year":"2020","unstructured":"Xiuyu Wu , Nan Jiang , and Yunfang Wu. 2020. A question type driven and copy loss enhanced framework for answer-agnostic neural question generation. ArXiv abs\/2005.11665 ( 2020 ). Xiuyu Wu, Nan Jiang, and Yunfang Wu. 2020. A question type driven and copy loss enhanced framework for answer-agnostic neural question generation. ArXiv abs\/2005.11665 (2020)."},{"key":"e_1_2_1_236_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/553"},{"key":"e_1_2_1_237_1","doi-asserted-by":"crossref","unstructured":"Chen Xing W. Wu Yu Wu J. Liu Yalou Huang M. Zhou and W. Ma. 2017. Topic aware neural response generation. In AAAI.  Chen Xing W. Wu Yu Wu J. Liu Yalou Huang M. Zhou and W. Ma. 2017. Topic aware neural response generation. In AAAI.","DOI":"10.1609\/aaai.v31i1.10981"},{"key":"e_1_2_1_238_1","unstructured":"Xinchen Yan Jimei Yang Kihyuk Sohn and H. Lee. 2016. Attribute2Image: Conditional image generation from visual attributes. ArXiv abs\/1512.00570 (2016).  Xinchen Yan Jimei Yang Kihyuk Sohn and H. Lee. 2016. Attribute2Image: Conditional image generation from visual attributes. ArXiv abs\/1512.00570 (2016)."},{"key":"e_1_2_1_239_1","volume-title":"ProphetNet: Predicting future n-gram for sequence-to-sequence pre-training. CoRR abs\/2001.04063","author":"Yan Yu","year":"2020","unstructured":"Yu Yan , Weizhen Qi , Yeyun Gong , Dayiheng Liu , Nan Duan , Jiusheng Chen , Ruofei Zhang , and Ming Zhou . 2020. ProphetNet: Predicting future n-gram for sequence-to-sequence pre-training. CoRR abs\/2001.04063 ( 2020 ). Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020. ProphetNet: Predicting future n-gram for sequence-to-sequence pre-training. CoRR abs\/2001.04063 (2020)."},{"key":"e_1_2_1_240_1","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems. 5753\u20135763","author":"Yang Zhilin","unstructured":"Zhilin Yang , Zihang Dai , Yiming Yang , Jaime Carbonell , Russ R. Salakhutdinov , and Quoc V. Le . 2019. Xlnet: Generalized autoregressive pretraining for language understanding . In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 5753\u20135763 . Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, and Quoc V. Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 5753\u20135763."},{"key":"e_1_2_1_241_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1096"},{"key":"e_1_2_1_242_1","volume-title":"Manning","author":"Yang Z.","year":"2018","unstructured":"Z. Yang , Peng Qi , Saizheng Zhang , Y. Bengio , W. Cohen , R. Salakhutdinov , and Christopher D . Manning . 2018 . HotpotQA: A dataset for diverse, explainable multi-hop question answering. ArXiv abs\/1809.09600 (2018). Z. Yang, Peng Qi, Saizheng Zhang, Y. Bengio, W. Cohen, R. Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. ArXiv abs\/1809.09600 (2018)."},{"key":"e_1_2_1_243_1","unstructured":"Kaichun Yao L. Zhang Tiejian Luo Lili Tao and Y. Wu. 2018. Teaching machines to ask questions. In IJCAI.  Kaichun Yao L. Zhang Tiejian Luo Lili Tao and Y. Wu. 2018. Teaching machines to ask questions. In IJCAI."},{"key":"e_1_2_1_244_1","volume-title":"Proceedings of QG2010: The Third Workshop on Question Generation. 68\u201375","author":"Yao Xuchen","year":"2010","unstructured":"Xuchen Yao . 2010 . Question generation with minimal recursion semantics . In Proceedings of QG2010: The Third Workshop on Question Generation. 68\u201375 . Xuchen Yao. 2010. Question generation with minimal recursion semantics. In Proceedings of QG2010: The Third Workshop on Question Generation. 68\u201375."},{"key":"e_1_2_1_245_1","doi-asserted-by":"publisher","DOI":"10.5087\/dad.2012.206"},{"key":"e_1_2_1_246_1","volume-title":"Proceedings of the 3rd Workshop on Question Generation. Citeseer, 68\u201375","author":"Yao Xuchen","year":"2010","unstructured":"Xuchen Yao and Yi Zhang . 2010 . Question generation with minimal recursion semantics . In Proceedings of the 3rd Workshop on Question Generation. Citeseer, 68\u201375 . Xuchen Yao and Yi Zhang. 2010. Question generation with minimal recursion semantics. In Proceedings of the 3rd Workshop on Question Generation. Citeseer, 68\u201375."},{"key":"e_1_2_1_247_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.26"},{"key":"e_1_2_1_248_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2603"},{"key":"e_1_2_1_249_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380126"},{"key":"e_1_2_1_250_1","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1181\u20131190","author":"Zamani Hamed","unstructured":"Hamed Zamani , Bhaskar Mitra , Everest Chen , Gord Lueck , Fernando Diaz , Paul N. Bennett , Nick Craswell , and Susan T. Dumais . 2020. Analyzing and learning from user interactions for search clarification . In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1181\u20131190 . Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, and Susan T. Dumais. 2020. Analyzing and learning from user interactions for search clarification. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1181\u20131190."},{"key":"e_1_2_1_251_1","doi-asserted-by":"crossref","unstructured":"Poorya Zaremoodi Wray L. Buntine and Gholamreza Haffari. 2018. Adaptive knowledge sharing in multi-task learning: Improving low-resource neural machine translation. In ACL.  Poorya Zaremoodi Wray L. Buntine and Gholamreza Haffari. 2018. Adaptive knowledge sharing in multi-task learning: Improving low-resource neural machine translation. In ACL.","DOI":"10.18653\/v1\/P18-2104"},{"key":"e_1_2_1_252_1","volume-title":"Liu","author":"Zhang Jingqing","year":"2019","unstructured":"Jingqing Zhang , Y. Zhao , Mohammad Saleh , and Peter J . Liu . 2019 . PEGASUS : Pre-training with extracted gap-sentences for abstractive summarization. ArXiv abs\/1912.08777 (2019). Jingqing Zhang, Y. Zhao, Mohammad Saleh, and Peter J. Liu. 2019. PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization. ArXiv abs\/1912.08777 (2019)."},{"key":"e_1_2_1_253_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3394052","article-title":"Dual-factor generation model for conversation","volume":"38","author":"Zhang R.","year":"2020","unstructured":"R. Zhang , J. Guo , Yixing Fan , Yanyan Lan , and Xueqi Cheng . 2020 . Dual-factor generation model for conversation . ACM Trans. Inf. Syst. 38 (2020), 1 \u2013 31 . R. Zhang, J. Guo, Yixing Fan, Yanyan Lan, and Xueqi Cheng. 2020. Dual-factor generation model for conversation. ACM Trans. Inf. Syst. 38 (2020), 1\u201331.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_2_1_254_1","doi-asserted-by":"crossref","unstructured":"R. Zhang J. Guo Y. Fan Yanyan Lan J. Xu and X. Cheng. 2018. Learning to control the specificity in neural response generation. In ACL.  R. Zhang J. Guo Y. Fan Yanyan Lan J. Xu and X. Cheng. 2018. Learning to control the specificity in neural response generation. In ACL.","DOI":"10.18653\/v1\/P18-1102"},{"key":"e_1_2_1_255_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1253"},{"key":"e_1_2_1_256_1","volume-title":"Automatic generation of grounded visual questions. ArXiv abs\/1612.06530","author":"Zhang S.","year":"2017","unstructured":"S. Zhang , Lizhen Qu , Shaodi You , Zhenglu Yang , and Jiawan Zhang . 2017. Automatic generation of grounded visual questions. ArXiv abs\/1612.06530 ( 2017 ). S. Zhang, Lizhen Qu, Shaodi You, Zhenglu Yang, and Jiawan Zhang. 2017. Automatic generation of grounded visual questions. ArXiv abs\/1612.06530 (2017)."},{"key":"e_1_2_1_257_1","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 177\u2013186","author":"Zhang Yongfeng","unstructured":"Yongfeng Zhang , Xu Chen , Qingyao Ai , Liu Yang , and W. Bruce Croft . 2018. Towards conversational search and recommendation: System ask, user respond . In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 177\u2013186 . Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W. Bruce Croft. 2018. Towards conversational search and recommendation: System ask, user respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 177\u2013186."},{"key":"e_1_2_1_258_1","volume-title":"A survey on multi-task learning. ArXiv abs\/1707.08114","author":"Zhang Y.","year":"2017","unstructured":"Y. Zhang and Qiang Yang . 2017. A survey on multi-task learning. ArXiv abs\/1707.08114 ( 2017 ). Y. Zhang and Qiang Yang. 2017. A survey on multi-task learning. ArXiv abs\/1707.08114 (2017)."},{"key":"e_1_2_1_259_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1424"},{"key":"e_1_2_1_260_1","doi-asserted-by":"publisher","DOI":"10.2298\/CSIS171121018Z"},{"key":"e_1_2_1_261_1","volume-title":"Natural Language Processing and Chinese Computing","author":"Zhou Qingyu","unstructured":"Qingyu Zhou , Nan Yang , Furu Wei , Chuanqi Tan , Hangbo Bao , and Ming Zhou . 2018. Neural question generation from text: A preliminary study . In Natural Language Processing and Chinese Computing , Xuanjing Huang, Jing Jiang, Dongyan Zhao, Yansong Feng, and Yu Hong (Eds.). Springer International Publishing , Cham , 662\u2013671. Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2018. Neural question generation from text: A preliminary study. In Natural Language Processing and Chinese Computing, Xuanjing Huang, Jing Jiang, Dongyan Zhao, Yansong Feng, and Yu Hong (Eds.). Springer International Publishing, Cham, 662\u2013671."},{"key":"e_1_2_1_262_1","doi-asserted-by":"crossref","unstructured":"Qingyu Zhou Nan Yang Furu Wei and M. Zhou. 2018. Sequential copying networks. In AAAI.  Qingyu Zhou Nan Yang Furu Wei and M. Zhou. 2018. Sequential copying networks. In AAAI.","DOI":"10.1609\/aaai.v32i1.11915"},{"key":"e_1_2_1_263_1","volume-title":"Multi-task learning with language modeling for question generation. ArXiv abs\/1908.11813","author":"Zhou Wenjie","year":"2019","unstructured":"Wenjie Zhou , Minghua Zhang , and Yunfang Wu. 2019. Multi-task learning with language modeling for question generation. ArXiv abs\/1908.11813 ( 2019 ). Wenjie Zhou, Minghua Zhang, and Yunfang Wu. 2019. Multi-task learning with language modeling for question generation. ArXiv abs\/1908.11813 (2019)."},{"key":"e_1_2_1_264_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1622"},{"key":"e_1_2_1_265_1","unstructured":"Kangli Zi Xingwu Sun Yanan Cao Shi Wang X. Feng Zhaobo Ma and C. Cao. 2019. Answer-focused and position-aware neural network for transfer learning in question generation. In KSEM.  Kangli Zi Xingwu Sun Yanan Cao Shi Wang X. Feng Zhaobo Ma and C. Cao. 2019. Answer-focused and position-aware neural network for transfer learning in question generation. In KSEM."},{"key":"e_1_2_1_266_1","doi-asserted-by":"crossref","unstructured":"Barret Zoph Deniz Yuret Jonathan May and K. Knight. 2016. Transfer learning for low-resource neural machine translation. ArXiv abs\/1604.02201 (2016).  Barret Zoph Deniz Yuret Jonathan May and K. Knight. 2016. Transfer learning for low-resource neural machine translation. ArXiv abs\/1604.02201 (2016).","DOI":"10.18653\/v1\/D16-1163"},{"key":"e_1_2_1_267_1","volume-title":"Pointing the unknown words. ArXiv abs\/1603.08148","author":"G\u00fcl\u00e7ehre \u00c7aglar","year":"2016","unstructured":"\u00c7aglar G\u00fcl\u00e7ehre , Sungjin Ahn , Ramesh Nallapati , Bowen Zhou , and Yoshua Bengio . 2016. Pointing the unknown words. ArXiv abs\/1603.08148 ( 2016 ). \u00c7aglar G\u00fcl\u00e7ehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the unknown words. ArXiv abs\/1603.08148 (2016)."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468889","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3468889","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:22Z","timestamp":1750191442000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3468889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":265,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,31]]}},"alternative-id":["10.1145\/3468889"],"URL":"https:\/\/doi.org\/10.1145\/3468889","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,8]]},"assertion":[{"value":"2021-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}