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Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context.
Evaluating rewards for question generation models
Tom Hosking and Sebastian Riedel. 2019 · 1902
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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 · 1902
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Learning to navigate unseen environments: Back translation with environmental dropout
Hao Tan, Licheng Yu, and Mohit Bansal. 2019 · 1904
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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 · 1905
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Synthetic qa corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 1906
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Harvesting paragraph-level question-answer pairs from wikipedia
Xinya Du and Claire Cardie. 2018 · 1917
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell. 1998 · 1998
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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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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Tri-training: Exploiting unlabeled data using three classifiers
Zhi-Hua Zhou and Ming Li. 2005 · 2005
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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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Deep questions without deep understanding
Igor Labutov, Sumit Basu, and Lucy Vanderwende. 2015 · 2015
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
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Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016 · 2016
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A simple, fast diverse decoding algorithm for neural generation
Jiwei Li, Will Monroe, and Dan Jurafsky. 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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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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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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Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
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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 · 2018
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Simple and effective semi-supervised question answering
Bhuwan Dhingra, Danish Danish, and Dheeraj Rajagopal. 2018 · 2018
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Speaker-follower models for vision-and-language navigation
Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, and Trevor Darrell. 2018 · 2018
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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 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
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Learning to paraphrase for question answering
Li Dong, Jonathan Mallinson, Siva Reddy, and Mirella Lapata. 2017 · 2017
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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 generation for question answering
Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017 · 2017
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Creativity: Generating diverse questions using variational autoencoders
Unnat Jain, Ziyu Zhang, and Alexander Schwing. 2017 · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Yanghoon Kim, Hwanhee Lee, Joongbo Shin, and Kyomin Jung. 2018 · 2018
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A framework for automatic question generation from text using deep reinforcement learning
Vishwajeet Kumar, Ganesh Ramakrishnan, and Yuan-Fang Li. 2018 · 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 · 2018
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Towards a better metric for evaluating question generation systems
Preksha Nema and Mitesh M Khapra. 2018 · 2018
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Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Self-training for jointly learning to ask and answer questions
Mrinmaya Sachan and Eric Xing. 2018 · 2018
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Leveraging context information for natural question generation
Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, and Daniel Gildea. 2018 · 2018
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Neural models for key phrase extraction and question generation
Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, and Yoshua Bengio. 2018 · 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 · 2018
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Learning to collaborate for question answering and asking
Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, and Ming Zhou. 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 W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 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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Generative question answering: Learning to answer the whole question
Mike Lewis and Angela Fan. 2019 · 2019
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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