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Most previous methods for text data augmentation are limited to simple tasks and weak baselines.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, T. Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 1905
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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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Tinybert: Distilling BERT for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2019 · 1909
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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 · 1909
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, W. Li, and Peter J. Liu. 2020 · 1910
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Probability of error of some adaptive pattern-recognition machines
H. J. Scudder III. 1965 · 1965
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
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Automatically generating extraction patterns from untagged text
Ellen Riloff. 1996 · 1996
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah A. Smith. 2020 · 2002
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2003
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Hieu Pham, Qizhe Xie, Zihang Dai, and Quoc V. Le. 2020 · 2003
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CG-BERT: conditional text generation with BERT for generalized few-shot intent detection
Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, and Philip S. Yu. 2020 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and B. Magnini. 2005 · 2005
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020c · 2006
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Knowledge-aware language model pretraining
Corby Rosset, Chenyan Xiong, M. Phan, Xia Song, Paul N. Bennett, and Saurabh Tiwary. 2020 · 2007
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and H. Schutze. 2021 · 2009
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The winograd schema challenge
H. Levesque, E. Davis, and L. Morgenstern. 2011 · 2011
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Semeval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
A. Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2012 · 2012
Cited alongside, same era.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al. 2013 · 2013
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and J. Dean. 2015 · 2015
Cited alongside, same era.
Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. 2015 · 2015
Cited alongside, same era.
That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
Insufficient data can also rock! learning to converse using smaller data with augmentation
Juntao Li, Lisong Qiu, Bo Tang, Dongmin Chen, Dongyan Zhao, and Rui Yan. 2019 · 2019
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Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and José Camacho-Collados. 2019 · 2019
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EDA: easy data augmentation techniques for boosting performance on text classification tasks
Jason W. Wei and Kai Zou. 2019 · 2019
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Data augmentation for spoken language understanding via joint variational generation
Kang Min Yoo, Youhyun Shin, and Sang-goo Lee. 2019 · 2019
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Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Deepstance at semeval-2016 task 6: Detecting stance in tweets using character and word-level cnns
Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. 2016 · 2016
Cited alongside, same era.
Data augmentation for low-resource neural machine translation
Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017 · 2017
Cited alongside, same era.
Globally normalized reader
Jonathan Raiman and John Miller. 2017 · 2017
Cited alongside, same era.
Sequence-to-sequence data augmentation for dialogue language understanding
Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018 · 2018
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and D. Roth. 2018 · 2018
Cited alongside, same era.
Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020b · 2020
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Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
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Sequence-level mixed sample data augmentation
Demi Guo, Yoon Kim, and Alexander M. Rush. 2020 · 2020
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Learning the difference that makes A difference with counterfactually-augmented data
Divyansh Kaushik, Eduard H. Hovy, and Zachary Chase Lipton. 2020 · 2020
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Tell me how to ask again: Question data augmentation with controllable rewriting in continuous space
Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, and Ming Zhou. 2020 · 2020
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How effective is task-agnostic data augmentation for pretrained transformers?
Shayne Longpre, Yu Wang, and Chris DuBois. 2020 · 2020
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Snippext: Semi-supervised opinion mining with augmented data
Zhengjie Miao, Yuliang Li, Xiaolan Wang, and Wang-Chiew Tan. 2020 · 2020
Later among the works it cites.
SSMBA: self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2020
Later among the works it cites.
PHICON: improving generalization of clinical text de-identification models via data augmentation
Xiang Yue and Shuang Zhou. 2020 · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le. 2020 · 2020
Later among the works it cites.
Gpt3mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyeong Park. 2021 · 2021
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