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As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models in the real world, e.g., making sure the models are robust against unseen or challenging scenarios.
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Deep reconstruction-classification networks for unsupervised domain adaptation
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Improved regularization of convolutional neural networks with cutout
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On calibration of modern neural networks
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Simple and scalable predictive uncertainty estimation using deep ensembles
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Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M. Dai, and Ian Goodfellow. 2017 · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto. 2017 · 2017
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Social bias in elicited natural language inferences
Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017 · 2017
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Select-additive learning: Improving generalization in multimodal sentiment analysis
Haohan Wang, Aaksha Meghawat, Louis-Philippe Morency, and Eric P. Xing. 2017 · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
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HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
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From lifestyle vlogs to everyday interactions
David F. Fouhey, Weicheng Kuo, Alexei A. Efros, and Jitendra Malik. 2018 · 2018
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Adversarial examples for natural language classification problems
Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. 2018 · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. 2018 · 2018
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Stress test evaluation for natural language inference
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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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Beyond sharing weights for deep domain adaptation
Artem Rozantsev, Mathieu Salzmann, and Pascal Fua. 2018 · 2018
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018a · 2018
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Neural machine translation of text from non-native speakers
Antonios Anastasopoulos, Alison Lui, Toan Q Nguyen, and David Chiang. 2019 · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Improving the robustness of question answering systems to question paraphrasing
Wee Chung Gan and Hwee Tou Ng. 2019 · 2019
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Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
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Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019 · 2019
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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. 2020 · 2020
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ToTTo: A controlled table-to-text generation dataset
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Robustness to modification with shared words in paraphrase identification
Zhouxing Shi and Minlie Huang. 2020 · 2020
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Robustness to spurious correlations via human annotations
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Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 2019
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Earlier isn’t always better: Sub-aspect analysis on corpus and system biases in summarization
Taehee Jung, Dongyeop Kang, Lucas Mentch, and Eduard Hovy. 2019 · 2019
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Training on synthetic noise improves robustness to natural noise in machine translation
Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, and Marjan Ghazvininejad. 2019 · 2019
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2019 · 2019
Cited alongside, same era.
Neural text summarization: A critical evaluation
Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
Discrete adversarial attacks and submodular optimization with applications to text classification
Qi Lei, Lingfei Wu, Pin-Yu Chen, Alex Dimakis, Inderjit S. Dhillon, and Michael J Witbrock. 2019 · 2019
Cited alongside, same era.
Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2019 · 2019
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Megha Srivastava, Tatsunori Hashimoto, and Percy Liang. 2020 · 2020
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Mind your inflections! Improving NLP for non-standard Englishes with Base-Inflection Encoding
Samson Tan, Shafiq Joty, Lav Varshney, and Min-Yen Kan. 2020b · 2020
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt. 2020 · 2020
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An empirical study on robustness to spurious correlations using pre-trained language models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He. 2020 · 2020
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Towards debiasing NLU models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020b · 2020
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CAT-gen: Improving robustness in NLP models via controlled adversarial text generation
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, and Ed Chi. 2020c · 2020
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Identifying spurious correlations for robust text classification
Zhao Wang and Aron Culotta. 2020a · 2020
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A survey of unsupervised deep domain adaptation
Garrett Wilson and Diane J. Cook. 2020 · 2020
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A closer look at accuracy vs. robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R Salakhutdinov, and Kamalika Chaudhuri. 2020 · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2020 · 2020
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Unsupervised domain adaptation for cross-lingual text labeling
Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero dos Santos, Kathleen McKeown, and Bing Xiang. 2020a · 2020
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Freelb: Enhanced adversarial training for language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Thomas Goldstein, and Jingjing Liu. 2020 · 2020
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Can we improve model robustness through secondary attribute counterfactuals?
Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed Chi, and Alex Beutel. 2021 · 2021
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Improving faithfulness in abstractive summarization with contrast candidate generation and selection
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021b · 2021
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Multi-granularity textual adversarial attack with behavior cloning
Yangyi Chen, Jin Su, and Wei Wei. 2021c · 2021
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Learning to recognize dialect features
Dorottya Demszky, Devyani Sharma, Jonathan Clark, Vinodkumar Prabhakaran, and Jacob Eisenstein. 2021 · 2021
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Nl-augmenter: A framework for task-sensitive natural language augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, and Samson Tan et al. 2021 · 2021
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Irm - when it works and when it doesn’t: A test case of natural language inference
Yana Dranker, He He, and Yonatan Belinkov. 2021 · 2021
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Statistics of robust optimization: A generalized empirical likelihood approach
John C Duchi, Peter W Glynn, and Hongseok Namkoong. 2021 · 2021
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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Robustness gym: Unifying the NLP evaluation landscape
Karan Goel, Nazneen Fatema Rajani, Jesse Vig, Zachary Taschdjian, Mohit Bansal, and Christopher Ré. 2021 · 2021
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In search of lost domain generalization
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Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. 2021 · 2021
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On robustness of neural semantic parsers
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Question and answer test-train overlap in open-domain question answering datasets
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Contextualized perturbation for textual adversarial attack
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