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Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.
Deep probabilistic logic: A unifying framework for indirect supervision
Hai Wang and Hoifung Poon. 2018 · 1902
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Markov logic networks
Matthew Richardson and Pedro Domingos. 2006 · 2006
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Jointly learning to extract and compress
Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein. 2011 · 2011
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Representing general relational knowledge in ConceptNet 5
Robyn Speer and Catherine Havasi. 2012 · 2012
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The cognitive processing of candidates during reading tests: Evidence from eye-tracking
Stephen Bax. 2013 · 2013
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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 · 2013
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Vader: A parsimonious rule-based model for sentiment analysis of social media text
CJ Hutto Eric Gilbert. 2014 · 2014
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Improving distant supervision for information extraction using label propagation through lists
Lidong Bing, Sneha Chaudhari, Richard Wang, and William Cohen. 2015 · 2015
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Concept-based summarization using integer linear programming: From concept pruning to multiple optimal solutions
Florian Boudin, Hugo Mougard, and Benoit Favre. 2015 · 2015
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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 · 2015
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An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson. 2015 · 2015
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Machine comprehension with discourse relations
Karthik Narasimhan and Regina Barzilay. 2015 · 2015
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Learning answer-entailing structures for machine comprehension
Mrinmaya Sachan, Kumar Dubey, Eric Xing, and Matthew Richardson. 2015 · 2015
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Machine comprehension with syntax, frames, and semantics
Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2015 · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016
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What’s in an explanation? Characterizing knowledge and inference requirements for elementary science exams
Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark. 2016 · 2016
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A corpus and evaluation framework for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 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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Emergent predication structure in hidden state vectors of neural readers
Hai Wang, Takeshi Onishi, Kevin Gimpel, and David McAllester. 2016 · 2016
Cited alongside, same era.
QUINT: Interpretable question answering over knowledge bases
Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2017 · 2017
Cited alongside, same era.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Coarse-to-fine question answering for long documents
Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant. 2017 · 2017
Cited alongside, same era.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Linguistic knowledge as memory for recurrent neural networks
SciTail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Later among the works it cites.
The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gáabor Melis, and Edward Grefenstette. 2018 · 2018
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Denoising distantly supervised open-domain question answering
Yankai Lin, Haozhe Ji, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
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SemEval-2018 Task 11: Machine comprehension using commonsense knowledge
Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, and Manfred Pinkal. 2018 · 2018
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Improving language understanding by generative pre-training
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Bhuwan Dhingra, Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2017 · 2017
Cited alongside, same era.
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.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
Stochastic answer networks for machine reading comprehension
Xiaodong Liu, Yelong Shen, Kevin Duh, and Jianfeng Gao. 2017 · 2017
Cited alongside, same era.
Question-answering with grammatically-interpretable representations
Hamid Palangi, Paul Smolensky, Xiaodong He, and Li Deng. 2017 · 2017
Cited alongside, same era.
Tell me why: Using question answering as distant supervision for answer justification
Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A Valenzuela-Escárcega, Peter Clark, and Michael Hammond. 2017 · 2017
Cited alongside, same era.
ConceptNet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Later among the works it cites.
Neural speed reading via Skim-RNN
Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi. 2018 · 2018
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Reading comprehension with graph-based temporal-casual reasoning
Yawei Sun, Gong Cheng, and Yuzhong Qu. 2018 · 2018
Later among the works it cites.
Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
John Wieting and Kevin Gimpel. 2018 · 2018
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
Dynamic fusion networks for machine reading comprehension
Yichong Xu, Jingjing Liu, Jianfeng Gao, Yelong Shen, and Xiaodong Liu. 2018 · 2018
Later among the works it cites.
TwoWingOS: A two-wing optimization strategy for evidential claim verification
Wenpeng Yin and Dan Roth. 2018 · 2018
Later among the works it cites.
Read+ verify: Machine reading comprehension with unanswerable questions
Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, and Dongsheng Li. 2019 · 2019
Closest in time.
Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning. 2019 · 2019
Closest in time.
DREAM: A challenge dataset and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019 · 2019
Closest in time.
Accurate supervised and semi-supervised machine reading for long documents
Daniel Hewlett, Llion Jones, Alexandre Lacoste, et al. 2017 · 2020
Closest in time.
An interpretable reasoning network for multi-relation question answering
Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2018 · 2022
Closest in time.