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Natural question generation (QG) aims to generate questions from a passage and an answer.
Bidirectional attentive memory networks for question answering over knowledge bases
Yu Chen, Lingfei Wu, and Mohammed J Zaki · 1903
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Yu Chen, Lingfei Wu, and Mohammed J Zaki · 1908
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Deep iterative and adaptive learning for graph neural networks
Yu Chen, Lingfei Wu, and Mohammed J Zaki · 1912
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Generating instruction automatically for the reading strategy of self-questioning
Jack Mostow and Wei Chen · 2009
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Good question! statistical ranking for question generation
Michael Heilman and Noah A Smith · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Automatic factual question generation from text
Michael Heilman · 2011
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Generating natural questions about an image
Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende · 2016
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Machine comprehension by text-to-text neural question generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, and Adam Trischler · 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
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Table-to-text: Describing table region with natural language
Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, and Tiejun Zhao · 2018
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Graph-to-sequence learning using gated graph neural networks
Daniel Beck, Gholamreza Haffari, and Trevor Cohn · 2018
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus
Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, and Yoshua Bengio · 2016
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Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an · 2017
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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A syntactic approach to domain-specific automatic question generation
Guy Danon and Mark Last · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Zero-shot question generation from knowledge graphs for unseen predicates and entity types
Hady Elsahar, Christophe Gravier, and Frederique Laforest · 2018
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A reinforcement learning framework for natural question generation using bi-discriminators
Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, and Xuanjing Huang · 2018
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Improving neural question generation using answer separation
Yanghoon Kim, Hwanhee Lee, Joongbo Shin, and Kyomin Jung · 2018
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Visual question generation as dual task of visual question answering
Yikang Li, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang, and Ming Zhou · 2018
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Contextualized non-local neural networks for sequence learning
Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, and Jackie Chi Kit Cheung · 2018
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Towards a better metric for evaluating question generation systems
Preksha Nema and Mitesh M Khapra · 2018
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Learning conditioned graph structures for interpretable visual question answering
Will Norcliffe-Brown, Stathis Vafeias, and Sarah Parisot · 2018
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Leveraging context information for natural question generation
Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, and Daniel Gildea · 2018
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Answer-focused and position-aware neural question generation
Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, and Shi Wang · 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
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Sequential copying networks
Qingyu Zhou, Nan Yang, Furu Wei, and Ming Zhou · 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
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Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao · 2019
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Reinforcement learning based text style transfer without parallel training corpus
Hongyu Gong, Suma Bhat, Lingfei Wu, Jinjun Xiong, and Wen-mei Hwu · 2019
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Learning to generate questions by learning what not to generate
Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, and Yu Xu · 2019
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Addressing semantic drift in question generation for semi-supervised question answering
Shiyue Zhang and Mohit Bansal · 2019
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