Fetching the paper…
Reading the bibliography…
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks.
Zhi-Xiu Ye, Qian Chen, Wen Wang, and Zhen-Hua Ling. 2019 · 1908
Earlier work this paper cites.
Learning small-size DNN with output-distribution-based criteria
Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. 2014 · 1914
Earlier work this paper cites.
Prior knowledge and reading comprehension test bias
Peter Johnston. 1984 · 1984
Earlier work this paper cites.
Measuring and explaining the reading threshold needed for english for academic purposes texts
Batia Laufer and Donald D Sim. 1985 · 1985
Earlier work this paper cites.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
Earlier work this paper cites.
Automatically generating extraction patterns from untagged text
Ellen Riloff. 1996 · 1996
Earlier work this paper cites.
Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
Earlier work this paper cites.
Issues, tasks and program structures to roadmap research in question & answering (q&a)
John Burger, Claire Cardie, Vinay Chaudhri, Robert Gaizauskas, Sanda Harabagiu, David Israel, Christian Jacquemin, Chin-Yew Lin, Steve Maiorano, George Miller, et al. 2001 · 2001
Earlier work this paper cites.
An overview of question and answering challenge (QAC) of the next NTCIR workshop
Jun-ichi Fukumoto and Tsuneaki Kato. 2001 · 2001
Earlier work this paper cites.
Reading comprehension requires knowledge of words and the world
Eric Donald Hirsch. 2003 · 2003
Earlier work this paper cites.
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2020 · 2009
Earlier work this paper cites.
The people’s republic of China national standard (GB/T 13745-2009): Classification and code of disciplines
Standardization Administration of China. 2009 · 2009
Earlier work this paper cites.
Improving machine reading comprehension with contextualized commonsense knowledge
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, and Claire Cardie. 2020a · 2009
Earlier work this paper cites.
Improved synthetic training for reading comprehension
Yanda Chen, Md Arafat Sultan, and Vittorio Castelli. 2020 · 2010
Earlier work this paper cites.
Self-training improves pre-training for natural language understanding
Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Ves Stoyanov, and Alexis Conneau. 2020 · 2010
Earlier work this paper cites.
MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana. 2014 · 2014
Earlier work this paper cites.
Overview of the NTCIR-11 QA-Lab task
Hideyuki Shibuki, Kotaro Sakamoto, Yoshinobu Kano, Teruko Mitamura, Madoka Ishioroshi, Kelly Y Itakura, Di Wang, Tatsunori Mori, and Noriko Kando. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Cited alongside, same era.
Overview of CLEF QA entrance exams task 2015
Alvaro Rodrigo, Anselmo Penas, Yusuke Miyao, Eduard H Hovy, and Noriko Kando. 2015 · 2015
Cited alongside, same era.
Taking up the gaokao challenge: An information retrieval approach
Gong Cheng, Weixi Zhu, Ziwei Wang, Jianghui Chen, and Yuzhong Qu. 2016 · 2016
Cited alongside, same era.
Combining retrieval, statistics, and inference to answer elementary science questions
Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter D Turney, and Daniel Khashabi. 2016 · 2016
Cited alongside, same era.
IJCNLP-2017 task 5: Multi-choice question answering in examinations
Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, and Zhuoyu Wei. 2017 · 2017
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.
GeoSQA: A benchmark for scenario-based question answering in the geography domain at high school level
Zixian Huang, Yulin Shen, Xiao Li, Yuang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, and Yuzhong Qu. 2019 · 2019
Later among the works it cites.
Improving question answering with external knowledge
Xiaoman Pan, Kai Sun, Dian Yu, Jianshu Chen, Heng Ji, Claire Cardie, and Dong Yu. 2019 · 2019
Later among the works it cites.
Transfer learning in natural language processing
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf. 2019 · 2019
Later among the works it cites.
HEAD-QA: A healthcare dataset for complex reasoning
David Vilares and Carlos Gómez-Rodríguez. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
RACE: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Cited alongside, same era.
Semi-supervised QA with generative domain-adaptive nets
Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, and William Cohen. 2017 · 2017
Cited alongside, same era.
Machine comprehension by text-to-text neural question generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, and Adam Trischler. 2017 · 2017
Cited alongside, same era.
Supervised and unsupervised transfer learning for question answering
Yu-An Chung, Hung-Yi Lee, and James Glass. 2018 · 2018
Cited alongside, same era.
Think you have solved question answering? try ARC, the AI2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
Simple and effective semi-supervised question answering
Bhuwan Dhingra, Danish Pruthi, and Dheeraj Rajagopal. 2018 · 2018
Cited alongside, same era.
Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Cited alongside, same era.
Zhao You, Dan Su, and Dong Yu. 2019 · 2019
Later among the works it cites.
Addressing semantic drift in question generation for semi-supervised question answering
Shiyue Zhang and Mohit Bansal. 2019 · 2019
Later among the works it cites.
Learning to ask unanswerable questions for machine reading comprehension
Haichao Zhu, Li Dong, Furu Wei, Wenhui Wang, Bing Qin, and Ting Liu. 2019 · 2019
Later among the works it cites.
Logic-guided data augmentation and regularization for consistent question answering
Akari Asai and Hannaneh Hajishirzi. 2020 · 2020
Later among the works it cites.
Revisiting pre-trained models for Chinese natural language processing
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020 · 2020
Later among the works it cites.
EXAMS: A multi-subject high school examinations dataset for cross-lingual and multilingual question answering
Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, and Preslav Nakov. 2020 · 2020
Later among the works it cites.
Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato. 2020 · 2020
Later among the works it cites.
Unsupervised domain adaptation of language models for reading comprehension
Kosuke Nishida, Kyosuke Nishida, Itsumi Saito, Hisako Asano, and Junji Tomita. 2020 · 2020
Later among the works it cites.
Unsupervised adaptation of question answering systems via generative self-training
Steven Rennie, Etienne Marcheret, Neil Mallinar, David Nahamoo, and Vaibhava Goel. 2020 · 2020
Later among the works it cites.
End-to-end synthetic data generation for domain adaptation of question answering systems
Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2020 · 2020
Later among the works it cites.
CLUE: A Chinese language understanding evaluation benchmark
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, and Zhenzhong Lan. 2020 · 2020
Later among the works it cites.
Model compression with two-stage multi-teacher knowledge distillation for web question answering system
Ze Yang, Linjun Shou, Ming Gong, Wutao Lin, and Daxin Jiang. 2020 · 2020
Later among the works it cites.
Robust machine reading comprehension by learning soft labels
Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, and Tiejun Zhao. 2020 · 2020
Later among the works it cites.
JEC-QA: A legal-domain question answering dataset
Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2020 · 2020
Later among the works it cites.