{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:43:52Z","timestamp":1792799032180,"version":"4.5.0"},"reference-count":159,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"German Federal Ministry of Education and Research","award":["13N15407"],"award-info":[{"award-number":["13N15407"]}],"id":[{"id":"https:\/\/ror.org\/04pz7b180","id-type":"ROR","asserted-by":"crossref"}]},{"name":"German Federal Ministry of Education and Research","id":[{"id":"https:\/\/ror.org\/04pz7b180","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Hessian Ministry of Higher Education, Research, Science"},{"name":"National Research Center for Applied Cybersecurity ATHENE","id":[{"id":"https:\/\/ror.org\/0378v2g76","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data to regularizing the objective, to limiting the amount of data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims at providing a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.<\/jats:p>","DOI":"10.1145\/3544558","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T08:06:21Z","timestamp":1655453181000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":359,"title":["A Survey on Data Augmentation for Text Classification"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2040-5609","authenticated-orcid":false,"given":"Markus","family":"Bayer","sequence":"first","affiliation":[{"name":"PEASEC*, Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0387-9597","authenticated-orcid":false,"given":"Marc-Andr\u00e9","family":"Kaufhold","sequence":"additional","affiliation":[{"name":"PEASEC*, Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-038X","authenticated-orcid":false,"given":"Christian","family":"Reuter","sequence":"additional","affiliation":[{"name":"PEASEC*, Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n18-2072"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1670"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/w17-3529"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.394"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/631"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_3_10_2","unstructured":"C. Coulombe. 2018. Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs. [Online]. Available: http:\/\/arxiv.org\/abs\/1812.04718"},{"key":"e_1_3_3_11_2","article-title":"When does label smoothing help?","volume":"32","author":"M\u00fcller R.","year":"2019","unstructured":"R. M\u00fcller, S. Kornblith, and G. Hinton. 2019. When does label smoothing help? Adv. Neural Inf. Process. Syst. 32, NeurIPS (2019). http:\/\/arxiv.org\/abs\/1906.02629","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.3115\/1073012.1073017"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aag2579"},{"key":"e_1_3_3_14_2","volume-title":"Proc. Eur. Conf. Inf. Syst.","author":"Bayer M.","year":"2021","unstructured":"M. Bayer, M.-A. Kaufhold, and C. Reuter. 2021. Information overload in crisis management: Bilingual evaluation of embedding models for clustering social media posts in emergencies. Proc. Eur. Conf. Inf. Syst. 1--18. http:\/\/www.peasec.de\/paper\/2021\/2021_BayerKaufholdReuter_InformationOverloadInCrisisManagementBilingualEvaluation_ECIS.pdf."},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102132"},{"key":"e_1_3_3_16_2","first-page":"6","article-title":"Cyber threat observatory: Design and evaluation of an interactive dashboard for computer emergency response teams","author":"Kaufhold M.-A.","year":"2022","unstructured":"M.-A. Kaufhold, A. S. Basyurt, K. Eyilmez, M. St\u00f6ttinger, and C. Reuter. 2022. Cyber threat observatory: Design and evaluation of an interactive dashboard for computer emergency response teams. In Proceedings of the European Conference on Information Systems. 6\u201318. Retrieved from https:\/\/aisel.aisnet.org\/ecis2022_rp\/99.","journal-title":"Proceedings of the European Conference on Information Systems"},{"key":"e_1_3_3_17_2","first-page":"1","article-title":"CYWARN: Strategy and technology development for cross-platform cyber situational awareness and actor-specific cyber threat communication","year":"2021","unstructured":"M.-A. Kaufhold, J. Fromm, T. Riebe, M. Mirbabaie, P. Kuehn, A. S. Basyurt, M. Bayer, M. St\u00f6ttinger, K. Eyilmez, R. M\u00f6ller, C. Fuch\u00df, S. Stieglitz, and C. Reuter. 2021. CYWARN: Strategy and technology development for cross-platform cyber situational awareness and actor-specific cyber threat communication. In Workshop-Proceedings Mensch and Computer. 1\u20139. Retrieved from https:\/\/dl.gi.de\/handle\/20.500.12116\/37412.","journal-title":"Workshop-Proceedings Mensch and Computer"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439726"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.1.1"},{"key":"e_1_3_3_21_2","unstructured":"A. Hern\u00e1ndez-Garc\u00eda and P. K\u00f6nig. 2018. Data augmentation instead of explicit regularization. arXiv:1806.03852. Retrieved from http:\/\/arxiv.org\/abs\/1806.03852."},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p18-2006"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1316"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202133"},{"key":"e_1_3_3_27_2","unstructured":"H. Inoue. 2018. Data augmentation by pairing samples for images classification. http:\/\/arxiv.org\/abs\/1801.02929."},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93040-4_28"},{"key":"e_1_3_3_31_2","unstructured":"N. Carlini F. Tram\u00e8r E. Wallace M. Jagielski A. Herbert-Voss K. Lee A. Roberts T. Brown D. Song \u00da. Erlingsson A. Oprea and C. Raffel. 2020. Extracting training data from large language models. In Proceedings of the 30th USENIX Security Symposium. 2633--2650. [Online]. Available: http:\/\/arxiv.org\/abs\/2012.07805"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6854671"},{"key":"e_1_3_3_33_2","article-title":"Vocal tract length perturbation (VTLP) improves speech recognition","author":"Jaitly N.","year":"2013","unstructured":"N. Jaitly and G. E. Hinton. 2013. Vocal tract length perturbation (VTLP) improves speech recognition. In Proceedings of the 30 th Int. Conf. Mach. Learn. 90 (2013), 42--51. http:\/\/www.iarpa.gov\/.","journal-title":"Proceedings of the 30 th Int. Conf. Mach. Learn"},{"key":"e_1_3_3_34_2","first-page":"810","article-title":"Data augmentation for low resource languages","author":"Ragni A.","year":"2014","unstructured":"A. Ragni, K. M. Knill, S. P. Rath, and M. J. F. Gales. 2014. Data augmentation for low resource languages. In Proceedings of the Proceedings of the Annual Conference of the International Speech Communication Association. 810\u2013814.","journal-title":"Proceedings of the Proceedings of the Annual Conference of the International Speech Communication Association"},{"key":"e_1_3_3_35_2","unstructured":"A. Hannun C. Case J. Casper B. Catanzaro G. Diamos E. Elsen R. Prenger S. Satheesh S. Sengupta A. Coates and A. Y. Ng. 2014. Deep speech: Scaling up end-to-end speech recognition. http:\/\/arxiv.org\/abs\/1412.5567."},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2015-711"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2015-711"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"e_1_3_3_39_2","volume-title":"Proceedings of 6th International Conference on Learning Representations","author":"Belinkov Y.","year":"2018","unstructured":"Y. Belinkov and Y. Bisk. 2018. Synthetic and natural noise both break neural machine translation. In Proceedings of 6th International Conference on Learning Representations."},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.deelio-1.4"},{"key":"e_1_3_3_41_2","article-title":"Adversarial training methods for semi-supervised text classification","author":"Miyato T.","year":"2017","unstructured":"T. Miyato, A. M. Dai, and I. Goodfellow. 2017. Adversarial training methods for semi-supervised text classification. In Proceedings of the 5th International Conference on Learning Representations.","journal-title":"Proceedings of the 5th International Conference on Learning Representations"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/e17-2004"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383552"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3406601.3406618"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6233"},{"key":"e_1_3_3_46_2","unstructured":"V. Kumar A. Choudhary and E. Cho. 2020. Data augmentation using pre-trained transformer models. http:\/\/arxiv.org\/abs\/2003.02245."},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-022-01553-3"},{"key":"e_1_3_3_48_2","unstructured":"S. T. Luu K. Van Nguyen and N. L.-T. Nguyen. 2020. Empirical study of text augmentation on social media text in vietnamese. http:\/\/arxiv.org\/abs\/2009.12319."},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.wnut-1.26"},{"key":"e_1_3_3_50_2","unstructured":"Z. Wu S. Wang J. Gu M. Khabsa F. Sun and H. Ma. 2020. CLEAR: Contrastive learning for sentence representation. http:\/\/arxiv.org\/abs\/2012.15466."},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358040"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-5748-4"},{"key":"e_1_3_3_53_2","article-title":"Unsupervised data augmentation for consistency training","author":"Xie Q.","year":"2019","unstructured":"Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le. 2019. Unsupervised data augmentation for consistency training. In Advances in Neural Information Processing Systems, vol. 2020-Dec. http:\/\/arxiv.org\/abs\/1904.12848.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_54_2","doi-asserted-by":"crossref","unstructured":"S. Choi M. Jeong H. Han and S.-W. Hwang. 2022. C 2 L: Causally contrastive learning for robust text classification. 2022. Available: www.aaai.org.","DOI":"10.1609\/aaai.v36i10.21296"},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1425"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1100"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.676"},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.447"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2016-727"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.5555\/2002736.2002793"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1093\/ijl\/3.4.235"},{"key":"e_1_3_3_62_2","first-page":"104","volume-title":"CEUR Workshop Proc","author":"Mosolova A. V.","year":"2018","unstructured":"A. V. Mosolova, V. V. Fomin, and I. Y. Bondarenko. 2018. Text augmentation for neural networks. In CEUR Workshop Proc. vol. 2268. 104\u2013109."},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.24507\/ijicic.15.01.227"},{"key":"e_1_3_3_64_2","first-page":"649","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Zhang X.","year":"2015","unstructured":"X. Zhang, J. Zhao, and Y. Lecun. 2015. Character-level convolutional networks for text classification. In Proceedings of the Advances in Neural Information Processing Systems, vol. 2015-Jan. 649\u2013657."},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_21"},{"key":"e_1_3_3_66_2","unstructured":"X. Zhang and Y. LeCun. 2015. Text understanding from scratch. http:\/\/arxiv.org\/abs\/1502.01710."},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.7494\/csci.2019.20.1.3023"},{"key":"e_1_3_3_68_2","article-title":"Learning data manipulation for augmentation and weighting","author":"Hu Z.","year":"2019","unstructured":"Z. Hu, B. Tan, R. Salakhutdinov, T. Mitchell, and E. P. Xing. 2019. Learning data manipulation for augmentation and weighting. In Advances in Neural Information Processing Systems, vol. 32. 15764--15775.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_69_2","article-title":"Text augmentation techniques for clinical case classification","author":"Ollagnier A.","year":"2020","unstructured":"A. Ollagnier and H. Williams. 2020. Text augmentation techniques for clinical case classification. CEUR Workshop Proceedings 2696, June (2020), 22--25. https:\/\/temu.bsc.es\/codiesp\/index.php\/2019\/09\/19\/resources\/.","journal-title":"CEUR Workshop Proceedings"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1080\/00437956.1954.11659520"},{"key":"e_1_3_3_71_2","first-page":"1","volume-title":"Proceedings of the Studies in Linguistic Analysis","author":"Firth J.","year":"1962","unstructured":"J. Firth. 1962. A synopsis of linguistic theory. In Proceedings of the Studies in Linguistic Analysis. 1\u201332."},{"key":"e_1_3_3_72_2","first-page":"90","volume-title":"Proceedings of the 1st Work. Trolling, Aggress. Cyberbullying","author":"Aroyehun S. T.","year":"2018","unstructured":"S. T. Aroyehun and A. Gelbukh. 2018. Aggression detection in social media: Using deep neural networks, data augmentation, and pseudo labeling. In Proceedings of the 1st Work. Trolling, Aggress. Cyberbullying. 90\u201397."},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d15-1306"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1780\/1\/012018"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n16-1018"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22747-0_7"},{"key":"e_1_3_3_77_2","volume-title":"North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf.","author":"Devlin J.","year":"2019","unstructured":"J. Devlin, M. W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf. 1, no. Mlm (2019), 4171--4186. http:\/\/arxiv.org\/abs\/1810.04805."},{"key":"e_1_3_3_78_2","unstructured":"Y. Qu D. Shen Y. Shen S. Sajeev J. Han and W. Chen. 2020. CoDA: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding. http:\/\/arxiv.org\/abs\/1810.04805."},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"e_1_3_3_80_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/d14-1162"},{"key":"e_1_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1555"},{"key":"e_1_3_3_82_2","first-page":"3237","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Ratner A. J.","year":"2017","unstructured":"A. J. Ratner, H. R. Ehrenberg, Z. Hussain, J. Dunnmon, and C. R\u00e9. 2017. Learning to compose domain-specific transformations for data augmentation. In Proceedings of the Advances in Neural Information Processing Systems, vol. 2017-Dec. 3237\u20133247. Retrieved from https:\/\/github.com\/HazyResearch\/tanda."},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-2090"},{"key":"e_1_3_3_84_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1545"},{"key":"e_1_3_3_85_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1102"},{"key":"e_1_3_3_86_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1272"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.212"},{"key":"e_1_3_3_88_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1334"},{"key":"e_1_3_3_89_2","unstructured":"Y. Goldberg. 2019. Assessing BERT's syntactic abilities. 2019. http:\/\/arxiv.org\/abs\/1901.05287."},{"key":"e_1_3_3_90_2","unstructured":"7th Int. Conf. Learn. Represent. 2019 What do you learn from context? Probing for sentence structure in contextualized word representations"},{"key":"e_1_3_3_91_2","volume-title":"Interpolation","author":"Steffensen J. F.","year":"2006","unstructured":"J. F. Steffensen. 2006. Interpolation. Courier Corporation. https:\/\/www.amazon.de\/Interpolation-Second-Mathematics-Steffensen-2006-03-17\/dp\/B01K3HR83G."},{"key":"e_1_3_3_92_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.307"},{"key":"e_1_3_3_93_2","unstructured":"H. Kim D. Woo S. J. Oh J.-W. Cha and Y.-S. Han. 2021. ALP: Data augmentation using lexicalized PCFGs for few-shot text classification. http:\/\/arxiv.org\/abs\/2112.11916."},{"issue":"1","key":"e_1_3_3_94_2","first-page":"1","article-title":"The efficacy of round-trip translation for MT evaluation","volume":"14","author":"Aiken M.","year":"2010","unstructured":"M. Aiken and M. Park. 2010. The efficacy of round-trip translation for MT evaluation. Transl. J. 14, 1 (2010), 1\u201310.","journal-title":"Transl. J."},{"key":"e_1_3_3_95_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations","year":"2018","unstructured":"A. W. Yu, D. Dohan, M. T. Luong, R. Zhao, K. Chen, M. Norouzi, and Q. V. Le. 2018. QaNet: Combining local convolution with global self-attention for reading comprehension. In Proceedings of the 6th International Conference on Learning Representations."},{"key":"e_1_3_3_96_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/e17-1101"},{"key":"e_1_3_3_97_2","volume-title":"Proceedings of the TREC","author":"Kruspe A.","year":"2018","unstructured":"A. Kruspe, J. Kersten, M. Wiegmann, B. Stein, and F. Klan. 2018. Classification of Incident-related Tweets\u202f: Tackling imbalanced training data using hybrid CNNs and translation-based data augmentation. In Proceedings of the TREC."},{"key":"e_1_3_3_98_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.194"},{"key":"e_1_3_3_99_2","unstructured":"Y. Wu M. Schuster Z. Chen Q. V. Le M. Norouzi W. Macherey M. Krikun Y. Cao Q. Gao K. Macherey J. Klingner A. Shah M. Johnson X. Liu \u0141. Kaiser S. Gouws Y. Kato T. Kudo H. Kazawa K. Stevens G. Kurian N. Patil W. Wang C. Young J. Smith J. Riesa A. Rudnick O. Vinyals G. Corrado M. Hughes and J. Dean. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. 1--23. http:\/\/arxiv.org\/abs\/1609.08144."},{"key":"e_1_3_3_100_2","doi-asserted-by":"crossref","unstructured":"H. Fang S. Wang M. Zhou J. Ding and P. Xie. 2020. CERT: Contrastive self-supervised learning for language understanding. Retrieved Jul. 14 2022 from https:\/\/github.com\/UCSD-AI4H\/.","DOI":"10.36227\/techrxiv.12308378.v1"},{"key":"e_1_3_3_101_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d17-1151"},{"key":"e_1_3_3_102_2","unstructured":"D. Shen M. Zheng Y. Shen Y. Qu and W. Chen. 2020. A simple but tough-to-beat data augmentation approach for natural language understanding and generation. http:\/\/arxiv.org\/abs\/2009.13818."},{"key":"e_1_3_3_103_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-5609"},{"key":"e_1_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00030"},{"key":"e_1_3_3_105_2","unstructured":"G. Raille S. Djambazovska and C. Musat. 2020. Fast cross-domain data augmentation through neural sentence editing. 2020. http:\/\/arxiv.org\/abs\/2003.10254."},{"key":"e_1_3_3_106_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"e_1_3_3_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.03.050"},{"key":"e_1_3_3_108_2","first-page":"8","volume-title":"Proceedings of the TREC","author":"Wang C.","year":"2020","unstructured":"C. Wang and D. Lillis. 2020. Classification for crisis-related tweets leveraging word embeddings and data augmentation. In Proceedings of the TREC. 8. Retrieved from https:\/\/trec.nist.gov\/."},{"key":"e_1_3_3_109_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61377-8_38"},{"key":"e_1_3_3_110_2","first-page":"37","article-title":"La g\u00e9n\u00e9ration de textes artificiels en substitution ou en compl\u00e9ment de donn\u00e9es d'apprentissage (Generating artificial texts as substitution or complement of training data)","author":"Claveau V.","year":"2021","unstructured":"V. Claveau, A. Chaffin, and E. Kijak. 2021. La g\u00e9n\u00e9ration de textes artificiels en substitution ou en compl\u00e9ment de donn\u00e9es d'apprentissage (Generating artificial texts as substitution or complement of training data). In Proceedings of the Actes de la 28e Conf\u00e9rence sur le Traitement Automatique des Langues Naturelles Lille. 37\u201349. http:\/\/arxiv.org\/abs\/2110.13016.","journal-title":"Proceedings of the Actes de la 28e Conf\u00e9rence sur le Traitement Automatique des Langues Naturelles Lille"},{"key":"e_1_3_3_111_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.726"},{"key":"e_1_3_3_112_2","first-page":"1","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Alec Radford I. S.","year":"2020","unstructured":"I. S. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, and Dario Amodei. 2020. Language models are unsupervised multitask learners. OpenAI Blog 1, May (2020), 1\u20137. Retrieved from https:\/\/github.com\/codelucas\/newspaper.","journal-title":"OpenAI Blog"},{"key":"e_1_3_3_113_2","unstructured":"V. Sanh L. Debut J. Chaumond and T. Wolf. 2019. DistilBERT a distilled version of BERT: Smaller faster cheaper and lighter. http:\/\/arxiv.org\/abs\/1910.01108."},{"key":"e_1_3_3_114_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.192"},{"key":"e_1_3_3_115_2","unstructured":"T. B. Brown B. Mann N. Ryder M. Subbiah J. Kaplan P. Dhariwal A. Neelakantan P. Shyam G. Sastry A. Askell S. Agarwal A. Herbert-Voss G. Krueger T. Henighan R. Child A. Ramesh D. M. Ziegler J. Wu C. Winter C. Hesse M. Chen E. Sigler M. Litwin S. Gray B. Chess J. Clark C. Berner S. McCandlish A. Radford I. Sutskever and D. Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems vol. 2020-Dec. [Online]. Available: http:\/\/arxiv.org\/abs\/2005.14165."},{"key":"e_1_3_3_116_2","unstructured":"K. Lee K. Guu L. He T. Dozat and H. W. Chung. 2021. Neural data augmentation via example extrapolation. http:\/\/arxiv.org\/abs\/2102.01335."},{"key":"e_1_3_3_117_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.488"},{"key":"e_1_3_3_118_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.64"},{"key":"e_1_3_3_119_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-6101"},{"key":"e_1_3_3_120_2","unstructured":"E. Schwartz L. Karlinsky J. Shtok S. Harary M. Marder R. Feris A. Kumar R. Giryes and A. M. Bronstein. 2018. Delta-encoder: An effective sample synthesis method for few-shot object recognition. http:\/\/arxiv.org\/abs\/1806.04734."},{"key":"e_1_3_3_121_2","unstructured":"C. Zhu Y. Cheng Z. Gan S. Sun T. Goldstein and J. Liu. 2019. FreeLB: Enhanced adversarial training for natural language understanding. http:\/\/arxiv.org\/abs\/1909.11764."},{"key":"e_1_3_3_122_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9569-8_10"},{"key":"e_1_3_3_123_2","unstructured":"T. Goldstein C. Studer and R. Baraniuk. 2014. A field guide to forward-backward splitting with a FASTA implementation. http:\/\/arxiv.org\/abs\/1411.3406."},{"key":"e_1_3_3_124_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Shafahi A.","year":"2019","unstructured":"A. Shafahi et al. 2019. Adversarial training for free!. In Proceedings of the Advances in Neural Information Processing Systems, vol. 32."},{"key":"e_1_3_3_125_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Zhang D.","year":"2019","unstructured":"D. Zhang, T. Zhang, Y. Lu, Z. Zhu, and B. Dong. 2019. You only propagate once: Accelerating adversarial training via maximal principle. In Proceedings of the Advances in Neural Information Processing Systems, vol. 32. Retrieved from https:\/\/github.com\/a1600012888\/YOPO-You-Only-Propagate-Once."},{"key":"e_1_3_3_126_2","volume-title":"Proceedings of the 4th International Conference on Learning Representations, ICLR 2016","author":"Miyato T.","year":"2016","unstructured":"T. Miyato, S. I. Maeda, M. Koyama, K. Nakae, and S. Ishii. 2016. Distributional smoothing with virtual adversarial training. In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016."},{"key":"e_1_3_3_127_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.197"},{"key":"e_1_3_3_128_2","first-page":"11387","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","author":"Wang D.","year":"2019","unstructured":"D. Wang, C. Gong, and Q. Liu. 2019. Improving neural language modeling via adversarial training. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019. 11387\u201311397."},{"key":"e_1_3_3_129_2","unstructured":"X. Liu H. Cheng P. He W. Chen Y. Wang H. Poon and J. Gao. 2020. Adversarial training for large neural language models. 2020. http:\/\/arxiv.org\/abs\/2004.08994."},{"key":"e_1_3_3_130_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.467"},{"key":"e_1_3_3_131_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.200"},{"key":"e_1_3_3_132_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/k16-1002"},{"key":"e_1_3_3_133_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_3_134_2","first-page":"11196","volume-title":"Proceedings of the 36th International Conference on Machine Learning","year":"2019","unstructured":"V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, and Y. Bengio. 2019. Manifold mixup: Better representations by interpolating hidden states. In Proceedings of the 36th International Conference on Machine Learning. 11196\u201311205."},{"key":"e_1_3_3_135_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITW.2015.7133169"},{"key":"e_1_3_3_136_2","unstructured":"R. Shwartz-Ziv and N. Tishby. 2017. Opening the black box of deep neural networks via information. http:\/\/arxiv.org\/abs\/1703.00810."},{"key":"e_1_3_3_137_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations","author":"Zhang H.","year":"2018","unstructured":"H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz. 2018. MixUp: Beyond empirical risk minimization. In Proceedings of the 6th International Conference on Learning Representations."},{"key":"e_1_3_3_138_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.305"},{"key":"e_1_3_3_139_2","unstructured":"H. Guo Y. Mao and R. Zhang. 2019. Augmenting data with mixup for sentence classification: An empirical study. http:\/\/arxiv.org\/abs\/1905.08941."},{"key":"e_1_3_3_140_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5822"},{"key":"e_1_3_3_141_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1356"},{"key":"e_1_3_3_142_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.95"},{"key":"e_1_3_3_143_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d19-1410"},{"key":"e_1_3_3_144_2","unstructured":"D. Hendrycks N. Mu E. D. Cubuk B. Zoph J. Gilmer and B. Lakshminarayanan. 2019. AugMix: A simple data processing method to improve robustness and uncertainty. http:\/\/arxiv.org\/abs\/1912.02781."},{"key":"e_1_3_3_145_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-4538-9_51"},{"key":"e_1_3_3_146_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.393"},{"key":"e_1_3_3_147_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.90"},{"key":"e_1_3_3_148_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d16-1244"},{"key":"e_1_3_3_149_2","doi-asserted-by":"publisher","DOI":"10.2307\/2981746"},{"key":"e_1_3_3_150_2","first-page":"2976","article-title":"Understanding black-box predictions via influence functions","author":"Koh P. W.","year":"2017","unstructured":"P. W. Koh and P. Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the 34th International Conference on Machine Learning. 2976\u20132987.","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"e_1_3_3_151_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.07.019"},{"key":"e_1_3_3_152_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1150"},{"key":"e_1_3_3_153_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-018-9549-x"},{"key":"e_1_3_3_154_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-03698-z"},{"key":"e_1_3_3_155_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-33341-6"},{"key":"e_1_3_3_156_2","unstructured":"J. Bragg A. Cohan K. Lo and I. Beltagy. 2021. FLEX: Unifying evaluation for few-Shot NLP. 2021. http:\/\/arxiv.org\/abs\/2107.07170."},{"key":"e_1_3_3_157_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.gem-1.10"},{"key":"e_1_3_3_158_2","first-page":"75","article-title":"NL-Augmenter: A framework for task-sensitive natural language augmentation","volume":"20","year":"2021","unstructured":"K. D. Dhole, V. Gangal, S. Gehrmann, A. Gupta, Z. Li, S. Mahamood, A. Mahendiran, S. Mille, A. Srivastava, S. Tan, T. Wu, J. Sohl-Dickstein, J. D. Choi, E. Hovy, O. Dusek, S. Ruder, S. Anand, N. Aneja, R. Banjade, L. Barthe, H. Behnke, I. Berlot-Attwell, C. Boyle, C. Brun, M. A. S. Cabezudo, S. Cahyawijaya, E. Chapuis, W. Che, M. Choudhary, C. Clauss, P. Colombo, F. Cornell, G. Dagan, M. Das, T. Dixit, T. Dopierre, P.-A. Dray, S. Dubey, T. Ekeinhor, M. Di Giovanni, R. Gupta, R. Gupta, L. Hamla, S. Han, F. Harel-Canada, A. Honore, I. Jindal, P. K. Joniak, D. Kleyko, V. Kovatchev, K. Krishna, A. Kumar, S. Langer, S. R. Lee, C. J. Levinson, H. Liang, K. Liang, Z. Liu, A. Lukyanenko, V. Marivate, G. de Melo, S. Meoni, M. Meyer, A. Mir, N. S. Moosavi, N. Muennighoff, T. S. H. Mun, K. Murray, M. Namysl, M. Obedkova, P. Oli, N. Pasricha, J. Pfister, R. Plant, V. Prabhu, V. Pais, L. Qin, S. Raji, P. K. Rajpoot, V. Raunak, R. Rinberg, N. Roberts, J. D. Rodriguez, C. Roux, V. P. H. S., A. B. Sai, R. M. Schmidt, T. Scialom, T. Sefara, S. N. Shamsi, X. Shen, H. Shi, Y. Shi, A. Shvets, N. Siegel, D. Sileo, J. Simon, C. Singh, R. Sitelew, P. Soni, T. Sorensen, W. Soto, A. Srivastava, K. A. Srivatsa, T. Sun, M. V. T, A. Tabassum, F. A. Tan, R. Teehan, M. Tiwari, M. Tolkiehn, A. Wang, Z. Wang, G. Wang, Z. J. Wang, F. Wei, B. Wilie, G. I. Winata, X. Wu, W. Wydma\u0144ski, T. Xie, U. Yaseen, M. Yee, J. Zhang, and Y. Zhang. 2021. NL-Augmenter: A framework for task-sensitive natural language augmentation. Nivranshu Pasricha 20 (2021), 75. http:\/\/arxiv.org\/abs\/2112.02721.","journal-title":"Nivranshu Pasricha"},{"key":"e_1_3_3_159_2","doi-asserted-by":"crossref","unstructured":"Z. Papakipos and J. Bitton. 2022. AugLy: Data augmentations for robustness. arXiv:2201.06494. Retrieved from http:\/\/arxiv.org\/abs\/2201.06494.","DOI":"10.1109\/CVPRW56347.2022.00027"},{"key":"e_1_3_3_160_2","unstructured":"I. Solaiman M. Brundage J. Clark A. Askell A. Herbert-Voss J. Wu A. Radford and J. Wang. 2019. Release strategies and the social impacts of language models. http:\/\/arxiv.org\/abs\/1908.09203."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3544558","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3544558","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T12:46:21Z","timestamp":1750164381000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3544558"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":159,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7,31]]}},"alternative-id":["10.1145\/3544558"],"URL":"https:\/\/doi.org\/10.1145\/3544558","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]},"assertion":[{"value":"2021-07-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}