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Data augmentation techniques are widely used in text classification tasks to improve the performance of classifiers, especially in low-resource scenarios.
Good-enough compositional data augmentation
Andreas, J. 2019 · 1904
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Unsupervised data augmentation for consistency training
Xie, Q.; Dai, Z.; Hovy, E.; Luong, M.-T.; and Le, Q. V. 2019 · 1904
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Augmenting data with mixup for sentence classification: An empirical study
Guo, H.; Mao, Y.; and Zhang, R. 2019 · 1905
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Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N.; and Gurevych, I. 2019 · 1908
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2019 · 1910
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WordNet: a lexical database for English
Miller, G. A. 1995 · 1995
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
Morris, J. X.; Lifland, E.; Yoo, J. Y.; Grigsby, J.; Jin, D.; and Qi, Y. 2020 · 2005
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Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering
Greene, D.; and Cunningham, P. 2006 · 2006
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Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
Sun, L.; Xia, C.; Yin, W.; Liang, T.; Yu, P. S.; and He, L. 2020 · 2010
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Model-portability experiments for textual temporal analysis
Kolomiyets, O.; Bethard, S.; and Moens, M.-F. 2011 · 2011
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Learning Word Vectors for Sentiment Analysis
Maas, A. L.; Daly, R. E.; Pham, P. T.; Huang, D.; Ng, A. Y.; and Potts, C. 2011 · 2011
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Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A. Y.; and Potts, C. 2013 · 2013
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Convolutional Neural Networks for Sentence Classification
Kim, Y. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Pennington, J.; Socher, R.; and Manning, C. D. 2014 · 2014
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
Wang, W. Y.; and Yang, D. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Zhang, X.; Zhao, J.; and LeCun, Y. 2015 · 2015
Cited alongside, same era.
Data recombination for neural semantic parsing
Jia, R.; and Liang, P. 2016 · 2016
Cited alongside, same era.
Recurrent neural network for text classification with multi-task learning
Qanet: Combining local convolution with global self-attention for reading comprehension
Yu, A. W.; Dohan, D.; Luong, M.-T.; Zhao, R.; Chen, K.; Norouzi, M.; and Le, Q. V. 2018 · 2018
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mixup: Beyond Empirical Risk Minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Wei, J.; and Zou, K. 2019 · 2019
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Do not have enough data? Deep learning to the rescue!
Anaby-Tavor, A.; Carmeli, B.; Goldbraich, E.; Kantor, A.; Kour, G.; Shlomov, S.; Tepper, N.; and Zwerdling, N. 2020 · 2020
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Liu, P.; Qiu, X.; and Huang, X. 2016 · 2016
Cited alongside, same era.
Improving Neural Machine Translation Models with Monolingual Data
Sennrich, R.; Haddow, B.; and Birch, A. 2016 · 2016
Cited alongside, same era.
Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification
Yu, J.; and Jiang, J. 2016 · 2016
Cited alongside, same era.
Dataset augmentation in feature space
DeVries, T.; and Taylor, G. W. 2017 · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
Cited alongside, same era.
Data augmentation for morphological reinflection
Silfverberg, M.; Wiemerslage, A.; Liu, L.; and Mao, L. J. 2017 · 2017
Cited alongside, same era.
Data noising as smoothing in neural network language models
Xie, Z.; Wang, S. I.; Li, J.; Lévy, D.; Nie, A.; Jurafsky, D.; and Ng, A. Y. 2017 · 2017
Cited alongside, same era.
GenAug: Data Augmentation for Finetuning Text Generators
Feng, S. Y.; Gangal, V.; Kang, D.; Mitamura, T.; and Hovy, E. 2020 · 2020
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Pretrained Transformers Improve Out-of-Distribution Robustness
Hendrycks, D.; Liu, X.; Wallace, E.; Dziedzic, A.; Krishnan, R.; and Song, D. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering
Karpukhin, V.; Oguz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2020
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Data Augmentation using Pre-trained Transformer Models
Kumar, V.; Choudhary, A.; and Cho, E. 2020 · 2020
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What makes for good views for contrastive learning?
Tian, Y.; Sun, C.; Poole, B.; Krishnan, D.; Schmid, C.; and Isola, P. 2020 · 2020
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The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT
Tiedemann, J. 2020 · 2020
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A survey on data augmentation for text classification
Bayer, M.; Kaufhold, M.-A.; and Reuter, C. 2021 · 2021
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An Empirical Survey of Data Augmentation for Limited Data Learning in NLP
Chen, J.; Tam, D.; Raffel, C.; Bansal, M.; and Yang, D. 2021 · 2021
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A Survey of Data Augmentation Approaches for NLP
Feng, S. Y.; Gangal, V.; Wei, J.; Chandar, S.; Vosoughi, S.; Mitamura, T.; and Hovy, E. 2021 · 2021
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