{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T21:46:54Z","timestamp":1794001614379,"version":"4.8.0"},"reference-count":142,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2027890"],"award-info":[{"award-number":["IIS-2027890"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.<\/jats:p>","DOI":"10.1186\/s40537-021-00492-0","type":"journal-article","created":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T03:02:59Z","timestamp":1626663779000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":583,"title":["Text Data Augmentation for Deep Learning"],"prefix":"10.1186","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-6861","authenticated-orcid":false,"given":"Connor","family":"Shorten","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taghi M.","family":"Khoshgoftaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borko","family":"Furht","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"492_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00392-9","author":"C Shorten","year":"2021","unstructured":"Shorten C, Khoshgoftaar T, Furht B. Deep learning applications for covid-19. J Big Data. 2021. https:\/\/doi.org\/10.1186\/s40537-020-00392-9.","journal-title":"J Big Data"},{"key":"492_CR2","unstructured":"Tang R, Nogueira R, Zhang E, Gupta N, Cam P, Cho K, Lin J. Rapidly bootstrapping a question answering dataset for covid-19. 2020. arXiv:2004.11339. Accessed Jul 2021"},{"key":"492_CR3","doi-asserted-by":"crossref","unstructured":"Cachola I, Lo K, Cohan A, Weld DS. TLDR: extreme summarization of scientific documents. 2020. arXiv:2004.15011. Accessed Jul 2021","DOI":"10.18653\/v1\/2020.findings-emnlp.428"},{"issue":"1","key":"492_CR4","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929\u201358 .","journal-title":"J Mach Learn Res"},{"key":"492_CR5","unstructured":"Kuka\u010dka J, Golkov V, Cremers D. Regularization for deep learning: a taxonomy 2017 . arXiv:1710.10686. Accessed Jul 2021"},{"key":"492_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar T. A survey on image data augmentation for deep learning. J Big Data. 2019;6:1\u201348.","journal-title":"J Big Data"},{"key":"492_CR7","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV. AutoAugment: learning augmentation policies from data 2018. arXiv:1805.09501. Accessed Jul 2021","DOI":"10.1109\/CVPR.2019.00020"},{"key":"492_CR8","unstructured":"Ho D, Liang E, Stoica I, Abbeel P, Chen X. Population based augmentation: efficient learning of augmentation policy schedules 2019. arXiv:1905.05393. Accessed Jul 2021"},{"key":"492_CR9","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Shlens J, Le QV. RandAugment: practical automated data augmentation with a reduced search space 2019. arXiv:1909.13719. Accessed Jul 2021","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"492_CR10","unstructured":"Clune J. AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence 2019. arXiv:1905.10985. Accessed Jul 2021"},{"key":"492_CR11","doi-asserted-by":"publisher","unstructured":"Wang R, Lehman J, Clune J, Stanley KO. Poet: Open-ended coevolution of environments and their optimized solutions. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO \u201919, pp. 142\u2013151. Association for Computing Machinery, New York, NY, USA 2019. https:\/\/doi.org\/10.1145\/3321707.3321799.","DOI":"10.1145\/3321707.3321799"},{"key":"492_CR12","unstructured":"Such FP, Rawal A, Lehman J, Stanley KO, Clune J. Generative teaching networks: accelerating neural architecture search by learning to generate synthetic training data 2019. arXiv:1912.07768. Accessed Jul 2021"},{"key":"492_CR13","unstructured":"Rawal A, Lehman J, Such FP, Clune J, Stanley KO. Synthetic petri dish: a novel surrogate model for rapid architecture search 2020. arXiv:2005.13092. Accessed Jul 2021"},{"key":"492_CR14","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, Hesse C, Chen M, Sigler E, Litwin M, Gray S, Chess B, Clark J, Berner C, McCandlish S, Radford A, Sutskever I, Amodei D. Language models are few-shot learners 2020. arXiv:2005.14165. Accessed Jul 2021"},{"key":"492_CR15","unstructured":"OpenAI: DALL.E: Creating Images from Text. OpenAI 2021. https:\/\/openai.com\/blog\/dall-e\/. Accessed Jul 2021"},{"key":"492_CR16","doi-asserted-by":"crossref","unstructured":"Feng SY, Gangal V, Wei J, Chandar S, Vosoughi S, Mitamura T, Hovy E. A Survey of Data Augmentation Approaches for NLP. 2021;2105:03075.","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"492_CR17","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 2009. Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"492_CR18","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423.","DOI":"10.18653\/v1\/N19-1423"},{"key":"492_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar T, Wang D. A survey of transfer learning. J Big Data. 2016. https:\/\/doi.org\/10.1186\/s40537-016-0043-6.","journal-title":"J Big Data."},{"key":"492_CR20","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Viualizing data using t-sne. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"492_CR21","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction 2018. arXiv:1802.03426. Accessed Jul 2021","DOI":"10.21105\/joss.00861"},{"key":"492_CR22","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. Towards causal representation learning 2021. arXiv:2102.11107. Accessed Jul 2021","DOI":"10.1109\/JPROC.2021.3058954"},{"key":"492_CR23","unstructured":"Levine S, Kumar A, Tucker G, Fu J. Offline Reinforcement learning: tutorial, review, and perspectives on open problems 2020. arXiv:2005.01643. Accessed Jul 2021"},{"key":"492_CR24","unstructured":"Kaushik D, Hovy E, Lipton ZC. Learning the difference that makes a difference with counterfactually-augmented data 2019. arXiv:1909.12434. Accessed Jul 2021"},{"key":"492_CR25","doi-asserted-by":"crossref","unstructured":"Liu Q, Kusner M, Blunsom P. Counterfactual data augmentation for neural machine translation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 187\u2013197. Association for Computational Linguistics, Online 2021. https:\/\/www.aclweb.org\/anthology\/2021.naacl-main.18. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.18"},{"key":"492_CR26","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H. Generating Datasets with Pretrained Language Models 2021. arXiv:2104.07540. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.emnlp-main.555"},{"key":"492_CR27","volume-title":"Causality: models, reasoning, and inference","author":"J Pearl","year":"2000","unstructured":"Pearl J. Causality: models, reasoning, and inference. USA: Cambridge University Press; 2000."},{"key":"492_CR28","doi-asserted-by":"publisher","unstructured":"Bender EM, Koller A. Climbing towards NLU: On meaning, form, and understanding in the age of data. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5185\u20135198. Association for Computational Linguistics, Online 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.463.","DOI":"10.18653\/v1\/2020.acl-main.463"},{"key":"492_CR29","doi-asserted-by":"crossref","unstructured":"Merrill W, Goldberg Y, Schwartz R, Smith NA. Provable limitations of acquiring meaning from ungrounded form: what will future language models understand? 2021. arXiv:2104.10809. Accessed Jul 2021","DOI":"10.1162\/tacl_a_00412"},{"key":"492_CR30","doi-asserted-by":"crossref","unstructured":"Weber L, Jumelet J, Bruni E, Hupkes D. Language modelling as a multi-task problem. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 2049\u20132060. Association for Computational Linguistics, Online 2021. https:\/\/www.aclweb.org\/anthology\/2021.eacl-main.176. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.eacl-main.176"},{"key":"492_CR31","doi-asserted-by":"publisher","unstructured":"Wang A, Singh A, Michael J, Hill F, Levy O, Bowman S. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks For NLP, pp. 353\u2013355. Association for Computational Linguistics, Brussels, Belgium 2018. https:\/\/doi.org\/10.18653\/v1\/W18-5446.","DOI":"10.18653\/v1\/W18-5446"},{"key":"492_CR32","doi-asserted-by":"crossref","unstructured":"Sarlin P-E, DeTone D, Malisiewicz T, Rabinovich A. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019. arXiv:1911.11763. Accessed Jul 2021","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"492_CR33","doi-asserted-by":"crossref","unstructured":"Petroni F, Piktus A, Fan A, Lewis P, Yazdani M, Cao ND, Thorne J, Jernite Y, Karpukhin V, Maillard J, Plachouras V, Rockt\u00e4schel T, Riedel S. KILT: a benchmark for knowledge intensive language tasks 2020. arXiv:2009.02252. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.200"},{"key":"492_CR34","doi-asserted-by":"publisher","unstructured":"Wei J, Zou K. EDA: Easy data augmentation techniques for boosting performance on text classification tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6382\u20136388. Association for Computational Linguistics, Hong Kong, China 2019. https:\/\/doi.org\/10.18653\/v1\/D19-1670.","DOI":"10.18653\/v1\/D19-1670"},{"key":"492_CR35","doi-asserted-by":"publisher","first-page":"8018","DOI":"10.1609\/aaai.v34i05.6311","volume":"34","author":"D Jin","year":"2020","unstructured":"Jin D, Jin Z, Zhou J, Szolovits P. Is bert really robust? a strong baseline for natural language attack on text classification and entailment. Proc Conf AAAI Artif Intell. 2020;34:8018\u201325. https:\/\/doi.org\/10.1609\/aaai.v34i05.6311.","journal-title":"Proc Conf AAAI Artif Intell"},{"key":"492_CR36","doi-asserted-by":"publisher","DOI":"10.2196\/17984","author":"I Spasic","year":"2020","unstructured":"Spasic I, Nenadic G. Clinical text data in machine learning: systematic review JMIR. Med Inform. 2020. https:\/\/doi.org\/10.2196\/17984.","journal-title":"Med Inform"},{"key":"492_CR37","doi-asserted-by":"publisher","unstructured":"Min J, McCoy RT, Das D, Pitler E, Linzen T. Syntactic data augmentation increases robustness to inference heuristics. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2339\u20132352. Association for Computational Linguistics, Online 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.212.","DOI":"10.18653\/v1\/2020.acl-main.212"},{"key":"492_CR38","doi-asserted-by":"publisher","unstructured":"McCoy T, Pavlick E, Linzen T. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3428\u20133448. Association for Computational Linguistics, Florence, Italy 2019. https:\/\/doi.org\/10.18653\/v1\/P19-1334.","DOI":"10.18653\/v1\/P19-1334"},{"key":"492_CR39","doi-asserted-by":"publisher","unstructured":"Ji S, Pan S, Cambria E, Marttinen P, Yu PS. A survey on knowledge graphs: representation, acquisition and applications 2020. https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843.","DOI":"10.1109\/TNNLS.2021.3070843"},{"issue":"11","key":"492_CR40","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller GA. Wordnet: a lexical database for english. Commun ACM. 1995;38(11):39\u201341. https:\/\/doi.org\/10.1145\/219717.219748.","journal-title":"Commun ACM"},{"issue":"2","key":"492_CR41","first-page":"313","volume":"19","author":"MP Marcus","year":"1993","unstructured":"Marcus MP, Marcinkiewicz MA, Santorini B. Building a large annotated corpus of english: the penn treebank. Comput Linguist. 1993;19(2):313\u201330.","journal-title":"Comput Linguist"},{"key":"492_CR42","doi-asserted-by":"crossref","unstructured":"Zeng X, Song X, Ma T, Pan X, Zhou Y, Hou Y, Zhang Z, Karypis G, Cheng F. Repurpose open data to discover therapeutics for COVID-19 using deep learning 2020. arXiv:2005.10831. Accessed Jul 2021","DOI":"10.1021\/acs.jproteome.0c00316"},{"key":"492_CR43","doi-asserted-by":"publisher","unstructured":"Huang L, Wu L, Wang L. Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward, 2020; pp. 5094\u20135107. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.457","DOI":"10.18653\/v1\/2020.acl-main.457"},{"key":"492_CR44","doi-asserted-by":"crossref","unstructured":"Glava\u0161, G., Vuli\u0107, I.: Is supervised syntactic parsing beneficial for language understanding tasks? an empirical investigation. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 3090\u20133104. Association for Computational Linguistics, Online 2021. https:\/\/www.aclweb.org\/anthology\/2021.eacl-main.270. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.eacl-main.270"},{"key":"492_CR45","unstructured":"Li MM, Huang K, Zitnik M. Representation learning for networks in biology and medicine: advancements, challenges, and opportunities 2021. arXiv:2104.04883. Accessed Jul 2021"},{"key":"492_CR46","doi-asserted-by":"crossref","unstructured":"Zhao T, Liu Y, Neves L, Woodford O, Jiang M, Shah N. Data augmentation for graph neural networks 2020. arXiv:2006.06830. Accessed Jul 2021","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"492_CR47","unstructured":"Kong K, Li G, Ding M, Wu Z, Zhu C, Ghanem B, Taylor G, Goldstein T. FLAG: adversarial data augmentation for graph neural networks 2020. arXiv:2010.09891. Accessed Jul 2021"},{"key":"492_CR48","doi-asserted-by":"publisher","unstructured":"Gopalan A, Juan D-C, Magalhaes CI, Ferng C-S, Heydon A, Lu C-T, Pham P, Yu G, Fan Y, Wang Y. Neural structured learning: Training neural networks with structured signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. WSDM \u201921, pp. 1150\u20131153. Association for Computing Machinery, New York, NY, USA 2021. https:\/\/doi.org\/10.1145\/3437963.3441666.","DOI":"10.1145\/3437963.3441666"},{"key":"492_CR49","doi-asserted-by":"crossref","unstructured":"Li J, Xiong C, Hoi S. Comatch: Semi-supervised learning with contrastive graph regularization. arXiv:2011.11183 . Accessed Jul 2021","DOI":"10.1109\/ICCV48922.2021.00934"},{"key":"492_CR50","unstructured":"Guo H, Mao Y, Zhang R. Augmenting data with mixup for sentence classification: an empirical study. arXiv:1905.08941 . Accessed Jul 2021"},{"key":"492_CR51","unstructured":"Cheung T-H, Yeung, D.-Y.: Modals: Modality-agnostic automated data augmentation in the latent space. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=XjYgR6gbCEc. Accessed Jul 2021"},{"key":"492_CR52","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D. Supervised contrastive learning 2020. arXiv:2004.11362. Accessed Jul 2021"},{"key":"492_CR53","doi-asserted-by":"crossref","unstructured":"Li Y, Hu G, Wang Y, Hospedales T, Robertson NM, Yang Y. DADA: differentiable automatic data augmentation 2020. arXiv:2003.03780. Accessed Jul 2021","DOI":"10.1007\/978-3-030-58542-6_35"},{"key":"492_CR54","unstructured":"Minderer M, Bachem O, Houlsby N, Tschannen M. Automatic shortcut removal for self-supervised representation learning 2020. arXiv:2002.08822. Accessed Jul 2021"},{"key":"492_CR55","unstructured":"Pham H, Wang X, Yang Y, Neubig G. Meta back-translation. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=3jjmdp7Hha. Accessed Jul 2021"},{"key":"492_CR56","doi-asserted-by":"crossref","unstructured":"Longpre S, Wang Y, DuBois C. How effective is task-agnostic data augmentation for pretrained transformers? arXiv:2010.01764 . Accessed Jul 2021","DOI":"10.18653\/v1\/2020.findings-emnlp.394"},{"key":"492_CR57","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks 2018. arXiv:1812.04948. Accessed Jul 2021","DOI":"10.1109\/CVPR.2019.00453"},{"key":"492_CR58","unstructured":"Gao L, Biderman S, Black S, Golding L, Hoppe T, Foster C, Phang J, He H, Thite A, Nabeshima N, Presser S, Leahy C. The pile: an 800GB dataset of diverse text for language modeling 2020. arXiv:2101.00027. Accessed Jul 2021"},{"key":"492_CR59","doi-asserted-by":"publisher","unstructured":"Gururangan S, Marasovi\u0107, A., Swayamdipta S, Lo K, Beltagy I, Downey D, Smith NA. Don\u2019t stop pretraining: Adapt language models to domains and tasks. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8342\u20138360. Association for Computational Linguistics, Online 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.740. Accessed Jul 2021","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"492_CR60","doi-asserted-by":"crossref","unstructured":"Wu X, Lv S, Zang L, Han J, Hu S. Conditional BERT contextual augmentation 2018. arXiv:1812.06705. Accessed Jul 2021","DOI":"10.1007\/978-3-030-22747-0_7"},{"key":"492_CR61","unstructured":"Scao TL, Rush AM. How many data points is a prompt worth? 2021. arXiv:2103.08493. Accessed Jul 2021"},{"key":"492_CR62","doi-asserted-by":"crossref","unstructured":"Lester B, Al-Rfou R, Constant N. The power of scale for parameter-efficient prompt tuning 2021. arXiv:2104.08691. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"492_CR63","unstructured":"Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, de Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S. Parameter-efficient transfer learning for NLP 2019. arXiv:1902.00751. Accessed Jul 2021"},{"key":"492_CR64","unstructured":"Zhang J, Zhao Y, Saleh M, Liu P. PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 11328\u201311339. PMLR? 2020. http:\/\/proceedings.mlr.press\/v119\/zhang20ae.html. Accessed Jul 2021"},{"key":"492_CR65","unstructured":"Zhong R, Lee K, Zhang Z, Klein D. Meta-tuning language models to answer prompts better 2021. arXiv:2104.04670. Accessed Jul 2021"},{"key":"492_CR66","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H. Exploiting cloze-questions for few-shot text classification and natural language inference. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 255\u2013269. Association for Computational Linguistics, Online 2021. https:\/\/www.aclweb.org\/anthology\/2021.eacl-main.20. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"492_CR67","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H. It\u2019s Not Just Size That Matters: small language models are also few-shot learners 2020. arXiv:2009.07118. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.185"},{"key":"492_CR68","doi-asserted-by":"crossref","unstructured":"Tam D, Menon RR, Bansal M, Srivastava S, Raffel C. Improving and simplifying pattern exploiting training 2021. arXiv:2103.11955. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.emnlp-main.407"},{"key":"492_CR69","doi-asserted-by":"crossref","unstructured":"Yoo KM, Park D, Kang J, Lee S-W, Park W. GPT3Mix: leveraging large-scale language models for text augmentation 2021. arXiv:2104.08826. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.findings-emnlp.192"},{"key":"492_CR70","doi-asserted-by":"publisher","unstructured":"Cer D, Yang Y, Kong, S.-y., Hua N, Limtiaco N, St.\u00a0John R, Constant N, Guajardo-Cespedes M, Yuan S, Tar C, Strope B, Kurzweil R. Universal sentence encoder for English. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 169\u2013174. Association for Computational Linguistics, Brussels, Belgium 2018. https:\/\/doi.org\/10.18653\/v1\/D18-2029. Accessed Jul 2021","DOI":"10.18653\/v1\/D18-2029"},{"key":"492_CR71","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong, China 2019. https:\/\/doi.org\/10.18653\/v1\/D19-1410. Accessed Jul 2021","DOI":"10.18653\/v1\/D19-1410"},{"key":"492_CR72","unstructured":"Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, K\u00fcttler H, Lewis M, Yih, W.-t., Rockt\u00e4schel T, Riedel S, Kiela D. Retrieval-augmented generation for knowledge-intensive nlp tasks. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 9459\u20139474. Curran Associates, Inc.,? 2020. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/6b493230205f780e1bc26945df7481e5-Paper.pdf. Accessed Jul 2021"},{"key":"492_CR73","unstructured":"Guu K, Lee K, Tung Z, Pasupat P, Chang, M.-W.: Realm: Retrieval-augmented language model pre-training. arXiv:2002.08909. Accessed Jul 2021"},{"key":"492_CR74","doi-asserted-by":"crossref","unstructured":"Shuster K, Poff S, Chen M, Kiela D, Weston J. Retrieval augmentation reduces hallucination in conversation 2021. arXiv:2104.07567. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.findings-emnlp.320"},{"key":"492_CR75","doi-asserted-by":"crossref","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, D\u2019Oliveira, R.G.L., Eichner H, Rouayheb SE, Evans D, Gardner J, Garrett Z, Gasc\u00f3n A, Ghazi B, Gibbons PB, Gruteser M, Harchaoui Z, He C, He L, Huo Z, Hutchinson B, Hsu J, Jaggi M, Javidi T, Joshi G, Khodak M, Kone\u010dn\u00fd, J., Korolova A, Koushanfar F, Koyejo S, Lepoint T, Liu Y, Mittal P, Mohri M, Nock R, \u00d6zg\u00fcr A, Pagh R, Raykova M, Qi H, Ramage D, Raskar R, Song D, Song W, Stich SU, Sun Z, Suresh AT, Tram\u00e8r F, Vepakomma P, Wang J, Xiong L, Xu Z, Yang Q, Yu FX, Yu H, Zhao S. Advances and open problems in federated learning 2019. arXiv:1912.04977. Accessed Jul 2021","DOI":"10.1561\/2200000083"},{"key":"492_CR76","unstructured":"Carlini N, Tramer F, Wallace E, Jagielski M, Herbert-Voss A, Lee K, Roberts A, Brown T, Song D, Erlingsson U, Oprea A, Raffel C. Extracting training data from large language models 2020. arXiv:2012.07805. Accessed Jul 2021"},{"key":"492_CR77","unstructured":"Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop 2015. arXiv:1503.02531. Accessed Jul 2021"},{"key":"492_CR78","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 2019. arXiv:1910.01108. Accessed Jul 2021"},{"key":"492_CR79","doi-asserted-by":"crossref","unstructured":"Chen X, He B, Hui K, Sun L, Sun Y. Simplified TinyBERT: knowledge distillation for document retrieval 2020. arXiv:2009.07531. Accessed Jul 2021","DOI":"10.1007\/978-3-030-72240-1_21"},{"key":"492_CR80","doi-asserted-by":"crossref","unstructured":"Xie Q, Luong M-T, Hovy E, Le QV. Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"492_CR81","unstructured":"M\u00fcller R, Kornblith S, Hinton G. When does label smoothing help? 2019. arXiv:1906.02629. Accessed Jul 2021"},{"key":"492_CR82","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, Chen X. Improved techniques for training gans. In: Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc.,? 2016. https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/8a3363abe792db2d8761d6403605aeb7-Paper.pdf. Accessed Jul 2021"},{"key":"492_CR83","doi-asserted-by":"crossref","unstructured":"Pham H, Dai Z, Xie Q, Luong M-T, Le QV. Meta pseudo labels 2020. arXiv:2003.10580. Accessed Jul 2021","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"492_CR84","unstructured":"Raghu A, Raghu M, Kornblith S, Duvenaud D, Hinton G. Teaching with commentaries. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=4RbdgBh9gE. Accessed Jul 2021"},{"key":"492_CR85","doi-asserted-by":"crossref","unstructured":"Thakur N, Reimers N, Daxenberger J, Gurevych I. Augmented SBERT: data augmentation method for improving bi-encoders for pairwise sentence scoring tasks 2020. arXiv:2010.08240. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.28"},{"key":"492_CR86","unstructured":"Arjovsky M. Out of Distribution generalization in machine learning 2021. arXiv:2103.02667. Accessed Jul 2021"},{"key":"492_CR87","unstructured":"Chollet F. On the measure of intelligence 2019. arXiv:1911.01547. Accessed Jul 2021"},{"key":"492_CR88","doi-asserted-by":"crossref","unstructured":"Nakkiran P, Kaplun G, Bansal Y, Yang T, Barak B, Sutskever I. Deep double descent: Where bigger models and more data hurt. In: International Conference on Learning Representations 2020. https:\/\/openreview.net\/forum?id=B1g5sA4twr. Accessed Jul 2021","DOI":"10.1088\/1742-5468\/ac3a74"},{"key":"492_CR89","unstructured":"Jia R, Liang P. Adversarial examples for evaluating reading comprehension systems. arXiv:1707.07328. Accessed Jul 2021"},{"key":"492_CR90","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Wu T, Guestrin C, Singh S. Beyond accuracy: Behavioral testing of NLP models with CheckList. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4902\u20134912. Association for Computational Linguistics, Online 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.442","DOI":"10.18653\/v1\/2020.acl-main.442"},{"key":"492_CR91","doi-asserted-by":"crossref","unstructured":"Clark P, Tafjord O, Richardson K. Transformers as soft reasoners over language 2020. arXiv:2002.05867. Accessed Jul 2021","DOI":"10.24963\/ijcai.2020\/537"},{"key":"492_CR92","unstructured":"Koh PW, Sagawa S, Marklund H, Xie SM, Zhang M, Balsubramani A, Hu, W.-h., Yasunaga M, Phillips RL, Beery S, Leskovec J, Kundaje A, Pierson E, Levine S, Finn C, Liang P. Wilds: a benchmark of in-the-wild distribution shifts. arXiv:2012.07421. Accessed Jul 2021"},{"key":"492_CR93","unstructured":"Nakkiran P, Neyshabur B, Sedghi H. The deep bootstrap framework: Good online learners are good offline generalizers. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=guetrIHLFGI. Accessed Jul 2021"},{"key":"492_CR94","unstructured":"Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models 2020. arXiv:2006.11239. Accessed Jul 2021"},{"key":"492_CR95","unstructured":"Garg S, Balakrishnan S, Kolter JZ, Lipton ZC. RATT: Leveraging unlabeled data to guarantee generalization 2021. arXiv:2105.00303. Accessed Jul 2021"},{"key":"492_CR96","doi-asserted-by":"publisher","unstructured":"Tan H, Bansal M. Vokenization: Improving language understanding with contextualized, visual-grounded supervision. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2066\u20132080. Association for Computational Linguistics, Online 2020. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.162","DOI":"10.18653\/v1\/2020.emnlp-main.162"},{"key":"492_CR97","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201914, pp. 2672\u20132680. MIT Press, Cambridge, MA, USA 2014"},{"key":"492_CR98","unstructured":"Clark K, Luong M-T, Le QV, Manning CD. Electra: Pre-training text encoders as discriminators rather than generators. In: International Conference on Learning Representations 2020. https:\/\/openreview.net\/forum?id=r1xMH1BtvB. Accessed Jul 2021"},{"key":"492_CR99","unstructured":"Zhang H, Zhang Z, Odena A, Lee H. Consistency regularization for generative adversarial networks. In: International Conference on Learning Representations 2020. https:\/\/openreview.net\/forum?id=S1lxKlSKPH. Accessed Jul 2021"},{"key":"492_CR100","unstructured":"Self-supervised learning: The Dark Matter of Intelligence. https:\/\/ai.facebook.com\/blog\/self-supervised-learning-the-dark-matter-of-intelligence. Accessed 14 May 2021."},{"key":"492_CR101","unstructured":"Ji CX, Oberst M, Kanjilal S, Sontag D. Trajectory inspection: A method for iterative clinician-driven design of reinforcement learning studies. AMIA 2021 Virtual Informatics Summit 2021"},{"key":"492_CR102","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","author":"A Johnson","year":"2016","unstructured":"Johnson A, Pollard T, Shen L, Lehman L-W, Feng M, Ghassemi M, Moody B, Szolovits P, Celi L, Mimic-iii MR. A freely accessible critical care database. Sci Data. 2016. https:\/\/doi.org\/10.1038\/sdata.2016.35.","journal-title":"Sci Data"},{"key":"492_CR103","unstructured":"Lachaux M-A, Roziere B, Chanussot L, Lample G. Unsupervised translation of programming languages 2020. arXiv:2006.03511. Accessed Jul 2021"},{"key":"492_CR104","doi-asserted-by":"crossref","unstructured":"Alberti C, Andor D, Pitler E, Devlin J, Collins M. Synthetic QA corpora generation with roundtrip consistency 2019. arXiv:1906.05416. Accessed Jul 2021","DOI":"10.18653\/v1\/P19-1620"},{"key":"492_CR105","unstructured":"Xie Q, Dai Z, Hovy E, Luong M-T, Le QV. Unsupervised data augmentation for consistency training 2019. arXiv:1904.12848. Accessed Jul 2021"},{"key":"492_CR106","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 1597\u20131607. PMLR? 2020. http:\/\/proceedings.mlr.press\/v119\/chen20j.html. Accessed Jul 2021"},{"key":"492_CR107","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"492_CR108","unstructured":"Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A. Unsupervised learning of visual features by contrasting cluster assignments 2020. arXiv:2006.09882. Accessed Jul 2021"},{"key":"492_CR109","unstructured":"Grill J-B, Strub F, Altch\u00e9, F., Tallec C, Richemond PH, Buchatskaya E, Doersch C, Pires BA, Guo ZD, Azar MG, Piot B, Kavukcuoglu K, Munos R, Valko M. Bootstrap your own latent: a new approach to self-supervised Learning 2020. arXiv:2006.07733. Accessed Jul 2021"},{"key":"492_CR110","doi-asserted-by":"crossref","unstructured":"Krishna K, Roy A, Iyyer M. Hurdles to progress in long-form question answering 2021. arXiv:2103.06332. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.393"},{"key":"492_CR111","unstructured":"Gunel B, Du J, Conneau A, Stoyanov V. Supervised contrastive learning for pre-trained language model fine-tuning. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=cu7IUiOhujH. Accessed Jul 2021"},{"key":"492_CR112","doi-asserted-by":"crossref","unstructured":"Rethmeier N, Augenstein I. A primer on contrastive pretraining in language processing: methods, lessons learned and perspectives 2021. arXiv:2102.12982. Accessed Jul 2021","DOI":"10.1145\/3561970"},{"key":"492_CR113","doi-asserted-by":"crossref","unstructured":"Thorne J, Vlachos A, Christodoulopoulos C, Mittal A. FEVER: a large-scale dataset for fact extraction and verification 2018. arXiv:1803.05355. Accessed Jul 2021","DOI":"10.18653\/v1\/N18-1074"},{"key":"492_CR114","unstructured":"Sinha A, Ayush K, Song J, Uzkent B, Jin H, Ermon S. Negative data augmentation. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=Ovp8dvB8IBH. Accessed Jul 2021"},{"key":"492_CR115","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106622","author":"X He","year":"2021","unstructured":"He X, Zhao K, Chu X. Automl: a survey of the state-of-the-art. Knowl-Based Syst. 2021. https:\/\/doi.org\/10.1016\/j.knosys.2020.106622.","journal-title":"Knowl-Based Syst"},{"key":"492_CR116","first-page":"1","volume":"18","author":"L Li","year":"2018","unstructured":"Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J Mach Learn Res. 2018;18:1\u201352.","journal-title":"J Mach Learn Res"},{"key":"492_CR117","unstructured":"Li L, Jamieson K, Rostamizadeh A, Gonina E, Hardt M, Recht B, Talwalkar A. A system for massively parallel hyperparameter tuning 2018. arXiv:1810.05934. Accessed Jul 2021"},{"key":"492_CR118","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2019","unstructured":"Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O. Spanbert: improving pre-training by representing and predicting spans. TACL. 2019;8:64\u201377.","journal-title":"TACL"},{"key":"492_CR119","doi-asserted-by":"crossref","unstructured":"Morris JX, Lifland E, Yoo JY, Grigsby J, Jin D, Qi Y. TextAttack: a framework for adversarial attacks, data augmentation, and adversarial training in NLP 2020. 2005.05909. Accessed Jul 2021","DOI":"10.18653\/v1\/2020.emnlp-demos.16"},{"key":"492_CR120","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks. In: International Conference on Learning Representations 2014. http:\/\/arxiv.org\/abs\/1312.6199. Accessed Jul 2021"},{"key":"492_CR121","unstructured":"Wang F, Zhang Y, Zheng Y, Ruan W. Gradient-guided dynamic efficient adversarial training. arXiv:2103.03076. Accessed Jul 2021"},{"key":"492_CR122","unstructured":"Qu Y, Shen D, Shen Y, Sajeev S, Chen W, Han J. Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding. In: International Conference on Learning Representations 2021. https:\/\/openreview.net\/forum?id=Ozk9MrX1hvA. Accessed Jul 2021"},{"key":"492_CR123","doi-asserted-by":"crossref","unstructured":"Xue L, Barua A, Constant N, Al-Rfou R, Narang S, Kale M, Roberts A, Raffel C. ByT5: Towards a token-free future with pre-trained byte-to-byte models 2021. arXiv:2105.13626. Accessed Jul 2021","DOI":"10.1162\/tacl_a_00461"},{"key":"492_CR124","doi-asserted-by":"crossref","unstructured":"Clark JH, Garrette D, Turc I, Wieting J. CANINE: pre-training an efficient tokenization-free encoder for language representation 2021. arXiv:2103.06874. Accessed Jul 2021","DOI":"10.1162\/tacl_a_00448"},{"key":"492_CR125","doi-asserted-by":"publisher","unstructured":"Prusa JD, Khoshgoftaar TM. Designing a better data representation for deep neural networks and text classification. https:\/\/doi.org\/10.1109\/IRI.2016.61. Accessed Jul 2021","DOI":"10.1109\/IRI.2016.61"},{"key":"492_CR126","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-017-0065-8","author":"J Prusa","year":"2017","unstructured":"Prusa J, Khoshgoftaar T. Improving deep neural network design with new text data representations. JBig Data. 2017. https:\/\/doi.org\/10.1186\/s40537-017-0065-8.","journal-title":"JBig Data."},{"key":"492_CR127","unstructured":"Choi D, Passos A, Shallue CJ, Dahl GE. Faster neural network training with data echoing 2019. arXiv:1907.05550. Accessed Jul 2021"},{"key":"492_CR128","unstructured":"Fort S, Brock A, Pascanu R, De S, Smith SL. Drawing multiple augmentation samples per image during training efficiently decreases test error 2021. arXiv:2105.13343. Accessed Jul 2021"},{"key":"492_CR129","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ben-Nun T, Hubara I, Giladi N, Hoefler T, Soudry D. Augment your batch: Improving generalization through instance repetition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020","DOI":"10.1109\/CVPR42600.2020.00815"},{"key":"492_CR130","doi-asserted-by":"crossref","unstructured":"Wei J, Huang C, Xu S, Vosoughi S. Text augmentation in a multi-task view. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 2888\u20132894. Association for Computational Linguistics, Online 2021. https:\/\/www.aclweb.org\/anthology\/2021.eacl-main.252","DOI":"10.18653\/v1\/2021.eacl-main.252"},{"key":"492_CR131","unstructured":"Kuchnik M, Smith V. Efficient augmentation via data subsampling. In: International Conference on Learning Representations 2019. https:\/\/openreview.net\/forum?id=Byxpfh0cFm. Accessed Jul 2021"},{"key":"492_CR132","doi-asserted-by":"crossref","unstructured":"Wei J, Huang C, Vosoughi S, Cheng Y, Xu S. Few-shot text classification with triplet networks, data augmentation, and curriculum learning 2021. arXiv:2103.07552. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.naacl-main.434"},{"key":"492_CR133","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"J Johnson","year":"2019","unstructured":"Johnson J, Khoshgoftaar T. Survey on deep learning with class imbalance. J Big Data. 2019;6:27. https:\/\/doi.org\/10.1186\/s40537-019-0192-5.","journal-title":"J Big Data"},{"key":"492_CR134","doi-asserted-by":"publisher","unstructured":"Prusa J, Khoshgoftaar TM, Dittman DJ, Napolitano A. Using random undersampling to alleviate class imbalance on tweet sentiment data. In: 2015 IEEE International Conference on Information Reuse and Integration, pp. 197\u2013202 2015. https:\/\/doi.org\/10.1109\/IRI.2015.39","DOI":"10.1109\/IRI.2015.39"},{"issue":"1","key":"492_CR135","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","volume":"40","author":"C Seiffert","year":"2010","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A. Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern Part A. 2010;40(1):185\u201397. https:\/\/doi.org\/10.1109\/TSMCA.2009.2029559.","journal-title":"IEEE Trans Syst Man Cybern Part A"},{"key":"492_CR136","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N Chawla","year":"2002","unstructured":"Chawla N, Bowyer K, Hall L, Kegelmeyer W. Smote: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357. https:\/\/doi.org\/10.1613\/jair.953.","journal-title":"J Artif Intell Res"},{"key":"492_CR137","unstructured":"Karras T, Aittala M, Hellsten J, Laine S, Lehtinen J, Aila T. Training generative adversarial networks with limited data. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12104\u201312114. Curran Associates, Inc., ??? 2020. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/8d30aa96e72440759f74bd2306c1fa3d-Paper.pdf. Accessed Jul 2021"},{"key":"492_CR138","unstructured":"Jun H, Child R, Chen M, Schulman J, Ramesh A, Radford A, Sutskever I. Distribution augmentation for generative modeling. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 5006\u20135019. PMLR? 2020. http:\/\/proceedings.mlr.press\/v119\/jun20a.html. Accessed Jul 2021"},{"key":"492_CR139","unstructured":"Image GPT. https:\/\/openai.com\/blog\/image-gpt\/. Accessed 14 May 2021."},{"key":"492_CR140","unstructured":"HuggingFace Datasets. https:\/\/huggingface.co\/docs\/datasets\/. Accessed 14 May 2021."},{"key":"492_CR141","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ. Exploring the limits of transfer learning with a unified text-to-text transformer 2019. arXiv:1910.10683. Accessed Jul 2021"},{"key":"492_CR142","doi-asserted-by":"crossref","unstructured":"Geva M, Katz U, Ben-Arie A, Berant J. What\u2019s in your Head? Emergent behaviour in multi-task transformer models 2021. arXiv:2104.06129. Accessed Jul 2021","DOI":"10.18653\/v1\/2021.emnlp-main.646"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00492-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-021-00492-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00492-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T16:26:04Z","timestamp":1672849564000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-021-00492-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,19]]},"references-count":142,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["492"],"URL":"https:\/\/doi.org\/10.1186\/s40537-021-00492-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-650804\/v1","asserted-by":"object"}]},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,19]]},"assertion":[{"value":"22 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"101"}}