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Generalization and robustness are both key desiderata for designing machine learning methods.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 · 1904
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 1905
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Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang. 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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Is bert really robust? natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2019 · 1907
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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. 2019c · 1907
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli, et al. 2019 · 1907
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Ernie 2.0: A continual pre-training framework for language understanding
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2019 · 1907
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2019a · 1908
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. 2019 · 1909
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Freelb: Enhanced adversarial training for language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Thomas Goldstein, and Jingjing Liu. 2019 · 1909
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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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Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2019 · 1911
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“cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
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The influence curve and its role in robust estimation
Frank R Hampel. 1974 · 1974
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018 · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Unilmv2: Pseudo-masked language models for unified language model pre-training
Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Songhao Piao, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2020 · 2002
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The microsoft toolkit of multi-task deep neural networks for natural language understanding
Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, and Jianfeng Gao. 2020 · 2002
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Yifei Min, Lin Chen, and Amin Karbasi. 2020 · 2002
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Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang. 2020 · 2002
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Introduction to the bio-entity recognition task at JNLPBA
Nigel Collier and Jin-Dong Kim. 2004 · 2004
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Deep learning based text classification: A comprehensive review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2020a · 2004
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Deep learning based text classification: a comprehensive review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2020b · 2004
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett. 2005 · 2005
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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SciTail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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The second PASCAL recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, and Danilo Giampiccolo. 2006 · 2006
Cited alongside, same era.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Cited alongside, same era.
Overview of biocreative ii gene mention recognition
Larry Smith, Lorraine Tanabe, Rie Ando, Cheng-Ju Kuo, I-Fang Chung, Chun-Nan Hsu, Yu-Shi Lin, Roman Klinger, Christoph Friedrich, Kuzman Ganchev, Manabu Torii, Hongfang Liu, Barry Haddow, Craig Struble, Richard Povinelli, Andreas Vlachos, William Baumgartner Jr, Lawrence Hunter, Bob Carpenter, and W. Wilbur. 2008 · 2008
Cited alongside, same era.
The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009 · 2009
Cited alongside, same era.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Cited alongside, same era.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Later among the works it cites.
Taku Kudo and John Richardson. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 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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A simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le. 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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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 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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Robust neural machine translation with doubly adversarial inputs
Yong Cheng, Lu Jiang, and Wolfgang Macherey. 2019 · 2019
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On the robustness of self-attentive models
Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, and Cho-Jui Hsieh. 2019 · 2019
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Disentangling adversarial robustness and generalization
David Stutz, Matthias Hein, and Bernt Schiele. 2019 · 2019
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Improving neural language modeling via adversarial training
Dilin Wang, Chengyue Gong, and Qiang Liu. 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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ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
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