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Data augmentation with mixup has shown to be effective on various computer vision tasks.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 1901
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Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 1905
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2019 · 1909
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Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran. 2001 · 2001
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
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Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2009
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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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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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Nsml: A machine learning platform that enables you to focus on your models
Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Donghyun Kwak, Jung-Woo Ha, et al. 2017 · 2017
Cited alongside, same era.
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
Cited alongside, same era.
Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio. 2019 · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. 2019 · 2019
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MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
Later among the works it cites.
Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song. 2020 · 2020
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Jason Bansal, Mohitand Weston, and Douwe Kiela. 2020 · 2020
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Hanjoo Kim, Minkyu Kim, Dongjoo Seo, Jinwoong Kim, Heungseok Park, Soeun Park, Hyunwoo Jo, KyungHyun Kim, Youngil Yang, Youngkwan Kim, et al. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
Cited alongside, same era.
Data augmentation revisited: Rethinking the distribution gap between clean and augmented data
Zhuoxun He, Lingxi Xie, Xin Chen, Ya Zhang, Yanfeng Wang, and Qi Tian. 2019 · 2019
Cited alongside, same era.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak. 2019 · 2019
Cited alongside, same era.
Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, and Marios Savvides. 2020 · 2020
Later among the works it cites.
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
Closest in time.
Consistency training with virtual adversarial discrete perturbation
Jungsoo Park, Gyuwan Kim, and Jaewoo Kang. 2021 · 2021
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Saliencymix: A saliency guided data augmentation strategy for better regularization
A F M Shahab Uddin, Mst. Sirazam Monira, Wheemyung Shin, TaeChoong Chung, and Sung-Ho Bae. 2021 · 2021
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