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We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 1901
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No training required: Exploring random encoders for sentence classification
John Wieting and Douwe Kiela. 2019 · 1901
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CODAH: an adversarially authored question-answer dataset for common sense
Michael Chen, Mike D’Arcy, Alisa Liu, Jared Fernandez, and Doug Downey. 2019 · 1904
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 1905
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Do neural dialog systems use the conversation history effectively? an empirical study
Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal, Sarath Chandar, and Yoshua Bengio. 2019 · 1906
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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 · 1906
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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. 2019b · 1907
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Scott Wen-tau Yih, and Yejin Choi. 2019 · 1908
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Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 1908
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2019 · 1909
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al. 1998 · 1998
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Adversarial filters of dataset biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E Peters, Ashish Sabharwal, and Yejin Choi. 2020 · 2002
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The Fifth PASCAL Recognizing Textual Entailment Challenge
Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009 · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
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Multi-column deep neural networks for image classification
Dan Cireşan, Ueli Meier, and Jürgen Schmidhuber. 2012 · 2012
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus. 2013 · 2013
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 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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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015 · 2015
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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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Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens Van Der Maaten. 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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The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 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
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
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Natural language inference over interaction space
Yichen Gong, Heng Luo, and Jian Zhang. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton. 2018 · 2018
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Never-ending learning
Tom Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Bo Yang, Justin Betteridge, Andrew Carlson, B Dalvi, Matt Gardner, Bryan Kisiel, et al. 2018 · 2018
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Build it, break it, fix it: Contesting secure development
Andrew Ruef, Michael Hicks, James Parker, Dave Levin, Michelle L Mazurek, and Piotr Mardziel. 2016 · 2016
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2017 · 2017
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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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
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2018 · 2018
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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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Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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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
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Learning from on-line user feedback in neural question answering on the web
Bernhard Kratzwald and Stefan Feuerriegel. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Combining fact extraction and verification with neural semantic matching networks
Yixin Nie, Haonan Chen, and Mohit Bansal. 2019 · 2019
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Trick me if you can: Human-in-the-loop generation of adversarial question answering examples
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 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 · 2019
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