{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:00Z","timestamp":1740160260937,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62266051","61966038"],"award-info":[{"award-number":["62266051","61966038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s13042-024-02249-6","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T20:22:56Z","timestamp":1718828576000},"page":"5427-5437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep question generation model based on dual attention guidance"],"prefix":"10.1007","volume":"15","author":[{"given":"Jinhong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"2249_CR1","doi-asserted-by":"crossref","unstructured":"Du X, Shao J, Cardie C (2017) Learning to ask: neural question generation for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1342\u20131352","DOI":"10.18653\/v1\/P17-1123"},{"key":"2249_CR2","doi-asserted-by":"crossref","unstructured":"Patel A, Bindal A, Kotek H, Klein C, Williams J (2021) Generating natural questions from images for multimodal assistants. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2270\u20132274","DOI":"10.1109\/ICASSP39728.2021.9413599"},{"key":"2249_CR3","doi-asserted-by":"crossref","unstructured":"Serban IV, Garc\u00eda-Dur\u00e1n A, Gulcehre C, Ahn S, Chandar S, Courville A, Bengio Y (2016) Generating factoid questions with recurrent neural networks: The 30M factoid question-answer corpus. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 588\u2013598","DOI":"10.18653\/v1\/P16-1056"},{"key":"2249_CR4","doi-asserted-by":"crossref","unstructured":"Wang Y, Liu C, Huang M, Nie L (2018) Learning to ask questions in open-domain conversational systems with typed decoders. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2193\u20132203","DOI":"10.18653\/v1\/P18-1204"},{"key":"2249_CR5","unstructured":"Tang D, Duan N, Qin T, Yan Z, Zhou M (2017) Question answering and question generation as dual tasks. arXiv preprint arXiv:1706.02027"},{"key":"2249_CR6","unstructured":"Danon G, Last M (2017) A syntactic approach to domain-specific automatic question generation. arXiv preprint arXiv:1712.09827"},{"key":"2249_CR7","doi-asserted-by":"crossref","unstructured":"Yao K, Zhang L, Luo T, Tao L, Wu Y (2018) Teaching machines to ask questions. In: IJCAI, pp. 4546\u20134552","DOI":"10.24963\/ijcai.2018\/632"},{"key":"2249_CR8","unstructured":"Mostow J, Chen W (2009) Generating instruction automatically for the reading strategy of self-questioning. In: Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. pp. 465\u2013472"},{"key":"2249_CR9","unstructured":"Kunichika H, Katayama T, Hirashima T, Takeuchi A (2004) Automated question generation methods for intelligent english learning systems and its evaluation. In: Proc. of ICCE, vol. 670"},{"issue":"3","key":"2249_CR10","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1017\/S1351324915000455","volume":"22","author":"Y Huang","year":"2016","unstructured":"Huang Y, He L (2016) Automatic generation of short answer questions for reading comprehension assessment. Nat Lang Eng 22(3):457\u2013489","journal-title":"Nat Lang Eng"},{"key":"2249_CR11","doi-asserted-by":"crossref","unstructured":"Zhou Q, Yang N, Wei F, Tan C, Bao H, Zhou M (2018) Neural question generation from text: a preliminary study. In: Natural Language Processing and Chinese Computing: 6th CCF International Conference, NLPCC 2017, Dalian, China, November 8\u201312, 2017, Proceedings 6, pp. 662\u2013671","DOI":"10.1007\/978-3-319-73618-1_56"},{"key":"2249_CR12","doi-asserted-by":"crossref","unstructured":"Kim Y, Lee H, Shin J, Jung K (2019) Improving neural question generation using answer separation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6602\u20136609","DOI":"10.1609\/aaai.v33i01.33016602"},{"key":"2249_CR13","doi-asserted-by":"crossref","unstructured":"Zhao Y, Ni X, Ding Y, Ke Q (2018) Paragraph-level neural question generation with maxout pointer and gated self-attention networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3901\u20133910","DOI":"10.18653\/v1\/D18-1424"},{"key":"2249_CR14","doi-asserted-by":"crossref","unstructured":"Liu B, Zhao M, Niu D, Lai K, He Y, Wei H, Xu Y (2019) Learning to generate questions by learningwhat not to generate. In: The World Wide Web Conference, pp. 1106\u20131118","DOI":"10.1145\/3308558.3313737"},{"key":"2249_CR15","doi-asserted-by":"crossref","unstructured":"Xie Y, Pan L, Wang D, Kan M-Y, Feng Y (2020) Exploring question-specific rewards for generating deep questions. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 2534\u20132546","DOI":"10.18653\/v1\/2020.coling-main.228"},{"key":"2249_CR16","doi-asserted-by":"crossref","unstructured":"Qiu L, Xiao Y, Qu Y, Zhou H, Li L, Zhang W, Yu Y (2019) Dynamically fused graph network for multi-hop reasoning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6140\u20136150","DOI":"10.18653\/v1\/P19-1617"},{"key":"2249_CR17","unstructured":"Chen Y, Wu L, Zaki MJ (2020) Reinforcement learning based graph-to-sequence model for natural question generation. In: 8th International Conference on Learning Representations"},{"key":"2249_CR18","doi-asserted-by":"crossref","unstructured":"Chai Z, Wan X (2020) Learning to ask more: Semi-autoregressive sequential question generation under dual-graph interaction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 225\u2013237","DOI":"10.18653\/v1\/2020.acl-main.21"},{"key":"2249_CR19","doi-asserted-by":"crossref","unstructured":"Pan L, Xie Y, Feng Y, Chua T-S, Kan M-Y (2020) Semantic graphs for generating deep questions. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1463\u20131475","DOI":"10.18653\/v1\/2020.acl-main.135"},{"key":"2249_CR20","doi-asserted-by":"crossref","unstructured":"Wang L, Xu Z, Lin Z, Zheng H, Shen Y (2020) Answer-driven deep question generation based on reinforcement learning. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 5159\u20135170","DOI":"10.18653\/v1\/2020.coling-main.452"},{"key":"2249_CR21","doi-asserted-by":"crossref","unstructured":"Huang Q, Fu M, Mo L, Cai Y, Xu J, Li P, Li Q, Leung H-f (2021) Entity guided question generation with contextual structure and sequence information capturing. In: Proceedings of the AAAI Conference on Artificial Intelligence, 35, pp. 13064\u201313072","DOI":"10.1609\/aaai.v35i14.17544"},{"issue":"11","key":"2249_CR22","doi-asserted-by":"publisher","first-page":"14628","DOI":"10.1007\/s10489-022-04260-2","volume":"53","author":"H Ma","year":"2023","unstructured":"Ma H, Wang J, Lin H, Xu B (2023) Graph augmented sequence-to-sequence model for neural question generation. Appl Intell 53(11):14628\u201314644","journal-title":"Appl Intell"},{"issue":"7","key":"2249_CR23","doi-asserted-by":"publisher","first-page":"8275","DOI":"10.1007\/s10489-022-03894-6","volume":"53","author":"P Shuai","year":"2023","unstructured":"Shuai P, Li L, Liu S, Shen J (2023) Qdg: a unified model for automatic question-distractor pairs generation. Appl Intell 53(7):8275\u20138285","journal-title":"Appl Intell"},{"issue":"2","key":"2249_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103232","volume":"60","author":"M Guan","year":"2023","unstructured":"Guan M, Mondal SK, Dai H-N, Bao H (2023) Reinforcement learning-driven deep question generation with rich semantics. Inf Process Manag 60(2):103232","journal-title":"Inf Process Manag"},{"key":"2249_CR25","doi-asserted-by":"crossref","unstructured":"Zhou W, Zhang M, Wu Y (2019) Question-type driven question generation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6032\u20136037","DOI":"10.18653\/v1\/D19-1622"},{"key":"2249_CR26","doi-asserted-by":"crossref","unstructured":"Zi K, Sun X, Cao Y, Wang S, Feng X, Ma Z, Cao C (2019) Answer-focused and position-aware neural network for transfer learning in question generation. In: Knowledge Science, Engineering and Management: 12th International Conference, KSEM 2019, Athens, Greece, August 28\u201330, 2019, Proceedings, Part II 12, pp. 339\u2013352","DOI":"10.1007\/978-3-030-29563-9_30"},{"key":"2249_CR27","doi-asserted-by":"crossref","unstructured":"Jia X, Zhou W, Sun X, Wu Y (2020) How to ask good questions? try to leverage paraphrases. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6130\u20136140","DOI":"10.18653\/v1\/2020.acl-main.545"},{"key":"2249_CR28","doi-asserted-by":"crossref","unstructured":"Song L, Wang Z, Hamza W, Zhang Y, Gildea D (2018) Leveraging context information for natural question generation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 569\u2013574","DOI":"10.18653\/v1\/N18-2090"},{"key":"2249_CR29","unstructured":"Heilman M, Smith NA (2010) Good question! statistical ranking for question generation. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 609\u2013617"},{"key":"2249_CR30","doi-asserted-by":"crossref","unstructured":"Labutov I, Basu S, Vanderwende L (2015) Deep questions without deep understanding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 889\u2013898","DOI":"10.3115\/v1\/P15-1086"},{"key":"2249_CR31","doi-asserted-by":"crossref","unstructured":"Kumar V, Boorla K, Meena Y, Ramakrishnan G, Li Y-F (2018) Automating reading comprehension by generating question and answer pairs. In: Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3\u20136, 2018, Proceedings, Part III 22, pp. 335\u2013348","DOI":"10.1007\/978-3-319-93040-4_27"},{"key":"2249_CR32","doi-asserted-by":"crossref","unstructured":"Zamani H, Dumais S, Craswell N, Bennett P, Lueck G (2020) Generating clarifying questions for information retrieval. In: Proceedings of the Web Conference 2020, pp. 418\u2013428","DOI":"10.1145\/3366423.3380126"},{"key":"2249_CR33","doi-asserted-by":"crossref","unstructured":"Li J, Gao Y, Bing L, King I, Lyu MR (2019) Improving question generation with to the point context. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3216\u20133226","DOI":"10.18653\/v1\/D19-1317"},{"key":"2249_CR34","doi-asserted-by":"publisher","first-page":"181041","DOI":"10.1109\/ACCESS.2019.2955156","volume":"7","author":"M Sadiq","year":"2019","unstructured":"Sadiq M, Shi D, Guo M, Cheng X (2019) Facial landmark detection via attention-adaptive deep network. IEEE Access 7:181041\u2013181050","journal-title":"IEEE Access"},{"key":"2249_CR35","doi-asserted-by":"publisher","first-page":"188771","DOI":"10.1109\/ACCESS.2020.3031722","volume":"8","author":"J Ma","year":"2020","unstructured":"Ma J, Jia C, Yang X, Cheng X, Li W, Zhang C (2020) A data-driven approach for collision risk early warning in vessel encounter situations using attention-bilstm. IEEE Access 8:188771\u2013188783","journal-title":"IEEE Access"},{"key":"2249_CR36","doi-asserted-by":"crossref","unstructured":"Su D, Xu Y, Dai W, Ji Z, Yu T, Fung P (2020) Multi-hop question generation with graph convolutional network. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4636\u20134647","DOI":"10.18653\/v1\/2020.findings-emnlp.416"},{"key":"2249_CR37","doi-asserted-by":"crossref","unstructured":"Su D, Xu P, Fung P (2022) Qa4qg: Using question answering to constrain multi-hop question generation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8232\u20138236","DOI":"10.1109\/ICASSP43922.2022.9747008"},{"key":"2249_CR38","doi-asserted-by":"crossref","unstructured":"Tang C, Zhang H, Loakman T, Lin C, Guerin F (2023) Enhancing dialogue generation via dynamic graph knowledge aggregation. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4604\u20134616","DOI":"10.18653\/v1\/2023.acl-long.253"},{"key":"2249_CR39","doi-asserted-by":"publisher","first-page":"9835","DOI":"10.1109\/ACCESS.2023.3239005","volume":"11","author":"S Matsumori","year":"2023","unstructured":"Matsumori S, Okuoka K, Shibata R, Inoue M, Fukuchi Y, Imai M (2023) Mask and cloze: automatic open cloze question generation using a masked language model. IEEE Access 11:9835\u20139850","journal-title":"IEEE Access"},{"key":"2249_CR40","doi-asserted-by":"crossref","unstructured":"Muse H, Bulathwela S, Yilmaz E (2023) Pre-training with scientific text improves educational question generation (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence, 37, pp. 16288\u201316289","DOI":"10.1609\/aaai.v37i13.27004"},{"key":"2249_CR41","doi-asserted-by":"crossref","unstructured":"Zhang C, Chen Y, Liu L, Liu Q, Zhou X (2022) Hico: Hierarchical contrastive learning for ultrasound video model pretraining. In: Proceedings of the Asian Conference on Computer Vision, pp. 229\u2013246","DOI":"10.1007\/978-3-031-26351-4_1"},{"key":"2249_CR42","unstructured":"An C, Feng J, Lv K, Kong L, Qiu X, Huang X (2022) Cont: Contrastive neural text generation. In: Advances in Neural Information Processing Systems 35, pp. 2197\u20132210"},{"key":"2249_CR43","unstructured":"De Marneffe M-C, Dozat T, Silveira N, Haverinen K, Ginter F, Nivre J, Manning CD (2014) Universal Stanford dependencies: a cross-linguistic typology. In: LREC, vol. 14, pp. 4585\u20134592"},{"key":"2249_CR44","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"2249_CR45","doi-asserted-by":"crossref","unstructured":"Denkowski M, Lavie A (2014) Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 376\u2013380","DOI":"10.3115\/v1\/W14-3348"},{"key":"2249_CR46","unstructured":"Lin C-Y (2004) Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381"},{"key":"2249_CR47","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02249-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02249-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02249-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:23:42Z","timestamp":1728451422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02249-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":47,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2249"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02249-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"9 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no conflicting interests known to the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}