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Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension.
A natural logic inference system
Yaroslav Fyodorov, Yoad Winter, and Nissim Francez · 2000
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Entailment, intensionality and text understanding
Cleo Condoravdi, Dick Crouch, Valeria De Paiva, Reinhard Stolle, and Daniel G Bobrow · 2003
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Recognising textual entailment with logical inference
Johan Bos and Katja Markert · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
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Clueweb09 data set, 2009
Jamie Callan, Mark Hoy, Changkuk Yoo, and Le Zhao · 2009
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An extended model of natural logic
Bill MacCartney and Christopher D Manning · 2009
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English gigaword fifth edition, 2011
Robert Parker, David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda · 2011
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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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
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Overview of clef qa entrance exams task 2015
Alvaro Rodrigo, Anselmo Penas, Yusuke Miyao, Eduard H Hovy, and Noriko Kando · 2015
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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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An analysis of prerequisite skills for reading comprehension
Saku Sugawara and Akiko Aizawa · 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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Lsdsem 2017: Exploring data generation methods for the story cloze test
Michael Bugert, Yevgeniy Puzikov, Andreas Rücklé, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez-Cámara, Daniil Sorokin, Maxime Peyrard, and Iryna Gurevych · 2017
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Pay attention to the ending: Strong neural baselines for the roc story cloze task
Zheng Cai, Lifu Tu, and Kevin Gimpel · 2017
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
Cited alongside, same era.
Question answering through transfer learning from large fine-grained supervision data
Sewon Min, Minjoon Seo, and Hannaneh Hajishirzi · 2017
Cited alongside, same era.
Story cloze task: Uw nlp system
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A Smith · 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
Cited alongside, same era.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2017
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Record: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme · 2018
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http://http://commoncrawl.org , 2019
Common crawl · 2019
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https://www.khanacademy.org/test-prep/lsat/lsat-lessons/logical-reasoning/a/logical-reasoning--article--question-type-catalog , 2019
Khan Academy · 2019
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https://www.lsac.org/lsat/taking-lsat/test-format/logical-reasoning , 2019a
Law School Admission Council · 2019
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Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2018
Cited alongside, same era.
The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein · 2018
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth · 2018
Cited alongside, same era.
The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette · 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
Cited alongside, same era.
https://www.lsac.org/lsat/taking-lsat/test-format/logical-reasoning/logical-reasoning-sample-questions , 2019b
Law School Admission Council · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
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Mmm: Multi-stage multi-task learning for multi-choice reading comprehension
Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, and Dilek Hakkani-tur · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Dream: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie · 2019
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Evalai: Towards better evaluation systems for ai agents
Deshraj Yadav, Rishabh Jain, Harsh Agrawal, Prithvijit Chattopadhyay, Taranjeet Singh, Akash Jain, Shiv Baran Singh, Stefan Lee, and Dhruv Batra · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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