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Prior work on automated question generation has almost exclusively focused on generating simple questions whose answers can be extracted from a single document.
Text generation from knowledge graphs with graph transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 1904
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Multi-hop question answering via reasoning chains
Jifan Chen, Shih-ting Lin, and Greg Durrett. 2019 · 1910
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Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2019 · 1911
Earlier work this paper cites.
Question generation from paragraphs: A tale of two hierarchical models
Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, and Gholamreza Haffari. 2019 · 1911
Earlier work this paper cites.
Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou. 2019 · 1911
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser. 1989 · 1989
Earlier work this paper cites.
Efficient backprop
Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller. 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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A simple yet strong pipeline for hotpotqa
Dirk Groeneveld, Tushar Khot, Ashish Sabharwal, et al. 2020 · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
C-Y Lin. 2004 · 2004
Earlier work this paper cites.
Graph sequential network for reasoning over sequences
Ming Tu, Jing Huang, Xiaodong He, and Bowen Zhou. 2020 · 2004
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Generating natural questions about an image
Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende. 2016 · 2016
Earlier work this paper cites.
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 · 2016
Cited alongside, same era.
Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Cited alongside, same era.
Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): A randomized controlled trial
Kathleen Kara Fitzpatrick, Alison Darcy, and Molly Vierhile. 2017 · 2017
Cited alongside, same era.
Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J Smola, and Le Song. 2018 · 2018
Later among the works it cites.
Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018a · 2018
Later among the works it cites.
Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018b · 2018
Later among the works it cites.
Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2018 · 2018
Later among the works it cites.
Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019 · 2019
Later among the works it cites.
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Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton. 2017 · 2017
Cited alongside, same era.
Question answering and question generation as dual tasks
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Ask the right questions: Active question reformulation with reinforcement learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Andrea Gesmundo, Neil Houlsby, Wojciech Gajewski, and Wei Wang. 2018 · 2018
Cited alongside, same era.
How much reading does reading comprehension require? A critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Parameter sharing methods for multilingual self-attentional translation models
Devendra Sachan and Graham Neubig. 2018 · 2018
Cited alongside, same era.
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
Later among the works it cites.
Learning data manipulation for augmentation and weighting
Zhiting Hu, Bowen Tan, Russ R Salakhutdinov, Tom M Mitchell, and Eric P Xing. 2019 · 2019
Later among the works it cites.
Improving neural question generation using answer separation
Yanghoon Kim, Hwanhee Lee, Joongbo Shin, and Kyomin Jung. 2019 · 2019
Later among the works it cites.
CLUTRR: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton. 2019 · 2019
Later among the works it cites.
Modeling graph structure in transformer for better AMR-to-text generation
Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019 · 2019
Later among the works it cites.
Graph transformer for graph-to-sequence learning
Deng Cai and Wai Lam. 2020 · 2020
Closest in time.
Reinforcement learning based graph-to-sequence model for natural question generation
Yu Chen, Lingfei Wu, and Mohammed J Zaki. 2020 · 2020
Closest in time.
Differentiable reasoning over a virtual knowledge base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, and William W Cohen. 2020 · 2020
Closest in time.
Semantic graphs for generating deep questions
Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, and Min-Yen Kan. 2020 · 2020
Closest in time.
Machine learning–driven language assessment
Burr Settles, Geoffrey T. LaFlair, and Masato Hagiwara. 2020 · 2020
Closest in time.
Generating multi-hop reasoning questions to improve machine reading comprehension
Jianxing Yu, Xiaojun Quan, Qinliang Su, and Jian Yin. 2020 · 2020
Closest in time.
Transformer-xh: Multi-evidence reasoning with extra hop attention
Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul Bennett, and Saurabh Tiwary. 2020 · 2020
Closest in time.