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Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019 · 1904
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Kermit: Generative insertion-based modeling for sequences
William Chan, Nikita Kitaev, Kelvin Guu, Mitchell Stern, and Jakob Uszkoreit. 2019 · 1906
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
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Explanation-based learning: An alternative view
Gerald DeJong and Raymond Mooney. 1986 · 1986
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005 · 2005
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Luke S. Zettlemoyer and Michael Collins. 2005 · 2005
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Learning a named entity tagger from gazetteers with the partial perceptron
Andrew Carlson, Scott Gaffney, and Flavian Vasile. 2009 · 2009
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Qed: A framework and dataset for explanations in question answering
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, and Michael Collins. 2020 · 2009
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee. 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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Learning from natural instructions
Dan Goldwasser and Dan Roth. 2014 · 2014
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The stanford corenlp natural language processing toolkit
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2015 · 2015
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Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016a · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Cited alongside, same era.
Learning to reason: End-to-end module networks for visual question answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. 2017 · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila. 2017 · 2017
Cited alongside, same era.
DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Self-assembling modular networks for interpretable multi-hop reasoning
Yichen Jiang and Mohit Bansal. 2019 · 2019
Later among the works it cites.
Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Human few-shot learning of compositional instructions
B. M. Lake, Tal Linzen, and M. Baroni. 2019 · 2019
Later among the works it cites.
Unsupervised question answering by cloze translation
Patrick Lewis, Ludovic Denoyer, and Sebastian Riedel. 2019 · 2019
Later among the works it cites.
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Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2017 · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola. 2017 · 2017
Cited alongside, same era.
Simple and effective semi-supervised question answering
Bhuwan Dhingra, Danish Danish, and Dheeraj Rajagopal. 2018 · 2018
Cited alongside, same era.
Training classifiers with natural language explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018 · 2018
Cited alongside, same era.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018b · 2018
Cited alongside, same era.
Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
Cited alongside, same era.
MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, Jeffrey Wu, R. Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Benefits of intermediate annotations in reading comprehension
Dheeru Dua, Sameer Singh, and Matt Gardner. 2020 · 2020
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Neural module networks for reasoning over text
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner. 2020 · 2020
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Harvesting and refining question-answer pairs for unsupervised QA
Zhongli Li, Wenhui Wang, Li Dong, Furu Wei, and Ke Xu. 2020 · 2020
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How can we accelerate progress towards human-like linguistic generalization?
Tal Linzen. 2020 · 2020
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Learning from explanations with neural execution tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, and Xiang Ren. 2020 · 2020
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