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Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs.
Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 1905
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Reinforced dynamic reasoning for conversational question generation
Boyuan Pan, Hao Li, Ziyu Yao, Deng Cai, and Huan Sun. 2019 · 1907
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Reinforcement learning based graph-to-sequence model for natural question generation
Yu Chen, Lingfei Wu, and Mohammed J Zaki. 2019 · 1908
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
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Effects of question-generation training on reading comprehension
Beth Davey and Susan McBride. 1986 · 1986
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Training question answering models from synthetic data
Raul Puri, Ryan Spring, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro. 2020 · 2002
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Reinforced multi-task approach for multi-hop question generation
Hardik Chauhan, Asif Ekbal, Pushpak Bhattacharyya, et al. 2020 · 2004
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Rouge: A package for automatic evaluation of summaries
C-Y LIN. 2004 · 2004
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The meteor metric for automatic evaluation of machine translation
Alon Lavie and Michael J Denkowski. 2009 · 2009
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Good question! statistical ranking for question generation
Michael Heilman and Noah A. Smith. 2010 · 2010
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Generating natural language questions to support learning on-line
David Lindberg, Fred Popowich, John Nesbit, and Phil Winne. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Deep questions without deep understanding
Igor Labutov, Sumit Basu, and Lucy Vanderwende. 2015 · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Towards a better metric for evaluating question generation systems
Preksha Nema and Mitesh M. Khapra. 2018b · 2018
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From eliza to xiaoice: challenges and opportunities with social chatbots
Heung-Yeung Shum, Xiao-dong He, and Di Li. 2018 · 2018
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A graph-to-sequence model for amr-to-text generation
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
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Question answering and question generation as dual tasks
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou. 2017 · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
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Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2017 · 2017
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Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2018 · 2018
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Teaching machines to ask questions
Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, and YanJun Wu. 2018 · 2018
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Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018 · 2018
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Learning to generate questions by learningwhat not to generate
Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, and Yu Xu. 2019 · 2019
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Let’s ask again: Refine network for automatic question generation
Preksha Nema, Akash Kumar Mohankumar, Mitesh M Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. 2019 · 2019
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Self-attention architectures for answer-agnostic neural question generation
Thomas Scialom, Benjamin Piwowarski, and Jacopo Staiano. 2019 · 2019
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Addressing semantic drift in question generation for semi-supervised question answering
Shiyue Zhang and Mohit Bansal. 2019 · 2019
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