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Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models.
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. 2019b · 1907
Earlier work this paper cites.
Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2019 · 1910
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Assessing the benchmarking capacity of machine reading comprehension datasets
Saku Sugawara, Pontus Stenetorp, Kentaro Inui, and Akiko Aizawa. 2019 · 1911
Earlier work this paper cites.
Building a large annotated corpus of English: The Penn Treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993 · 1993
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
Earlier work this paper cites.
Cheap and fast – but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Ng. 2008 · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, R. Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li. 2009 · 2009
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009 · 2009
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MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013 · 2013
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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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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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A thorough examination of the CNN/Daily Mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016 · 2016
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MS MARCO: A human generated MAchine Reading COmprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 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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Towards linguistically generalizable NLP systems: A workshop and shared task
Allyson Ettinger, Sudha Rao, Hal Daumé III, and Emily M. Bender. 2017 · 2017
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017 · 2017
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Cited alongside, same era.
NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Cited alongside, same era.
Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Cited alongside, same era.
QuAC: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Think you have solved question answering? Try ARC, the AI2 reasoning challenge
SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
Later among the works it cites.
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 · 2018
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CODAH: An adversarially-authored question answering dataset for common sense
Michael Chen, Mike D’Arcy, Alisa Liu, Jared Fernandez, and Doug Downey. 2019 · 2019
Later among the works it cites.
Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, and Matt Gardner. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
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Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Strength in numbers: Trading-off robustness and computation via adversarially-trained ensembles
Edward Grefenstette, Robert Stanforth, Brendan O’Donoghue, Jonathan Uesato, Grzegorz Swirszcz, and Pushmeet Kohli. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 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.
The NarrativeQA reading comprehension challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
Cited alongside, same era.
Efficient and robust question answering from minimal context over documents
Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018 · 2018
Cited alongside, same era.
Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston. 2019 · 2019
Later among the works it cites.
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
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
Later among the works it cites.
Inoculation by fine-tuning: A method for analyzing challenge datasets
Nelson F. Liu, Roy Schwartz, and Noah A. Smith. 2019a · 2019
Later among the works it cites.
CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
Later among the works it cites.
The FEVER2.0 shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 2019
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Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
Later among the works it cites.
HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Later among the works it cites.
Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.
Interpretation of natural language rules in conversational machine reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, and Sebastian Riedel. 2018 · 2097
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