Fetching the paper…
Reading the bibliography…
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail.
G. G. Sahin, M. Steedman, Data augmentation via dependency tree morphing for low-resource languages , arXiv abs/1903.09460 (2019) · 1903
Earlier work this paper cites.
H. Guo, Y. Mao, R. Zhang, Augmenting data with mixup for sentence classification: An empirical study , arXiv abs/1905.08941 (2019) · 1905
Earlier work this paper cites.
V. Sanh, L. Debut, J. Chaumond, T. Wolf, Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter , CoRR abs/1910.01108 (2019) · 1910
Earlier work this paper cites.
doi:10.1145/219717.219748
G. A. Miller, Wordnet: A lexical database for english , Commun. ACM 38 (11) (1995) 39–41 · 1995
Earlier work this paper cites.
doi:10.3115/980845.980860
C. F. Baker, C. J. Fillmore, J. B. Lowe, The Berkeley FrameNet project , in: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1, Association for Computational Linguistics, Montreal, Quebec, Canada, 1998, pp. 86–90 · 1998
Earlier work this paper cites.
J. Wang, H.-C. Chen, R. Radach, A. Inhoff, Reading Chinese script: A cognitive analysis, Psychology Press, 1999
1999
Earlier work this paper cites.
doi:10.3115/1073012.1073020
R. Barzilay, K. R. McKeown, Extracting paraphrases from a parallel corpus , in: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Toulouse, France, 2001, pp. 50–57 · 2001
Earlier work this paper cites.
G. Raille, S. Djambazovska, C. Musat, Fast cross-domain data augmentation through neural sentence editing , arXiv abs/2003.10254 (2020) · 2003
Earlier work this paper cites.
V. Kumar, A. Choudhary, E. Cho, Data augmentation using pre-trained transformer models , arXiv abs/2003.02245 (2020) · 2003
Earlier work this paper cites.
B. Peng, C. Zhu, M. Zeng, J. Gao, Data augmentation for spoken language understanding via pretrained models , arXiv abs/2004.13952 (2020) · 2004
Earlier work this paper cites.
M. S. Bari, M. T. Mohiuddin, S. R. Joty, Multimix: A robust data augmentation strategy for cross-lingual NLP , CoRR abs/2004.13240 (2020) · 2004
Earlier work this paper cites.
K. K. Schuler, VerbNet: A broad-coverage, comprehensive verb lexicon, University of Pennsylvania, 2005
2005
Earlier work this paper cites.
S. Nishikawa, R. Ri, Y. Tsuruoka, Data augmentation for learning bilingual word embeddings with unsupervised machine translation , CoRR abs/2006.00262 (2020) · 2006
Earlier work this paper cites.
S. Shehnepoor, R. Togneri, W. Liu, M. Bennamoun, Gangster: A fraud review detector based on regulated GAN with data augmentation , CoRR abs/2006.06561 (2020) · 2006
Earlier work this paper cites.
M. Regina, M. Meyer, S. Goutal, Text data augmentation: Towards better detection of spear-phishing emails , arXiv abs/2007.02033 (2020) · 2007
Earlier work this paper cites.
C. Rastogi, N. Mofid, F. Hsiao, Can we achieve more with less? exploring data augmentation for toxic comment classification , arXiv abs/2007.00875 (2020) · 2007
Earlier work this paper cites.
X. Mou, B. Sigouin, I. Steenstra, H. Su, Multimodal dialogue state tracking by QA approach with data augmentation , arXiv abs/2007.09903 (2020) · 2007
Earlier work this paper cites.
B. Tarján, G. Szaszák, T. Fegyó, P. Mihajlik, Deep transformer based data augmentation with subword units for morphologically rich online ASR , arXiv abs/2007.06949 (2020) · 2007
Earlier work this paper cites.
D. Zhang, T. Li, H. Zhang, B. Yin, On data augmentation for extreme multi-label classification , arXiv abs/2009.10778 (2020) · 2009
Earlier work this paper cites.
Q. Liu, W. Guan, S. Li, F. Cheng, D. Kawahara, S. Kurohashi, Reverse operation based data augmentation for solving math word problems , CoRR abs/2010.01556 (2020) · 2010
Earlier work this paper cites.
doi:10.1162/coli_a_00002
N. Madnani, B. J. Dorr, Generating phrasal and sentential paraphrases: A survey of data-driven methods , Computational Linguistics 36 (3) (2010) 341–387 · 2010
Earlier work this paper cites.
D. Ramirez-Echavarria, A. Bikakis, L. Dickens, R. Miller, A. Vlachidis, On the effects of knowledge-augmented data in word embeddings , CoRR abs/2010.01745 (2020) · 2010
Earlier work this paper cites.
S. Montella, B. Fabre, T. Urvoy, J. Heinecke, L. M. Rojas-Barahona, Denoising pre-training and data augmentation strategies for enhanced RDF verbalization with transformers , arXiv abs/2012.00571 (2020) · 2012
Earlier work this paper cites.
Y. Chen, S. Lu, F. Yang, X. Huang, X. Fan, C. Guo, Pattern-aware data augmentation for query rewriting in voice assistant systems , arXiv abs/2012.11468 (2020) · 2012
Earlier work this paper cites.
C. Si, Z. Zhang, F. Qi, Z. Liu, Y. Wang, Q. Liu, M. Sun, Better robustness by more coverage: Adversarial training with mixup augmentation for robust fine-tuning , arXiv abs/2012.15699 (2020) · 2012
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality , in: C. J. C. Burges, L. Bottou, Z. Ghahramani, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, 2013, pp. 3111–3119. URL https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, Y. Bengio, Generative adversarial networks , CoRR abs/1406.2661 (2014) · 2014
Earlier work this paper cites.
X. Zhang, J. J. Zhao, Y. LeCun, Character-level convolutional networks for text classification , in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 649–657. URL https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html
2015
Earlier work this paper cites.
doi:10.18653/v1/d15-1306
W. Y. Wang, D. Yang, That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets , in: L. Màrquez, C. Callison-Burch, J. Su, D. Pighin, Y. Marton (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, 2015, pp. 2557–2563 · 2015
Earlier work this paper cites.
J. Mueller, A. Thyagarajan, Siamese recurrent architectures for learning sentence similarity , in: D. Schuurmans, M. P. Wellman (Eds.), Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, AAAI Press, 2016, pp. 2786–2792. URL http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12195
2016
Earlier work this paper cites.
doi:10.18653/v1/P16-1009
R. Sennrich, B. Haddow, A. Birch, Improving neural machine translation models with monolingual data , in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Berlin, Germany, 2016, pp. 86–96 · 2016
Earlier work this paper cites.
doi:10.18653/v1/p16-1009
R. Sennrich, B. Haddow, A. Birch, Improving neural machine translation models with monolingual data , in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers, The Association for Computer Linguistics, 2016 · 2016
Earlier work this paper cites.
Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen, Y. Lu, Z. Jin, Improved relation classification by deep recurrent neural networks with data augmentation , in: N. Calzolari, Y. Matsumoto, R. Prasad (Eds.), COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan, ACL, 2016, pp. 1461–1470. URL https://aclanthology.org/C16-1138/
2016
Earlier work this paper cites.
Z. Xie, S. I. Wang, J. Li, D. Lévy, A. Nie, D. Jurafsky, A. Y. Ng, Data noising as smoothing in neural network language models , in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017. URL https://openreview.net/forum?id=H1VyHY9gg
2017
Earlier work this paper cites.
J. Kukacka, V. Golkov, D. Cremers, Regularization for deep learning: A taxonomy , CoRR abs/1710.10686 (2017) · 2017
Earlier work this paper cites.
C. Coulombe, Text data augmentation made simple by leveraging NLP cloud apis , ArXiv abs/1812.04718 (2018) · 2018
Earlier work this paper cites.
Y. Hou, Y. Liu, W. Che, T. Liu, Sequence-to-sequence data augmentation for dialogue language understanding , in: E. M. Bender, L. Derczynski, P. Isabelle (Eds.), Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, Association for Computational Linguistics, 2018, pp. 1234–1245. URL https://aclanthology.org/C18-1105/
2018
Earlier work this paper cites.
doi:10.18653/v1/P18-1225
D. Kang, T. Khot, A. Sabharwal, E. H. Hovy, Adventure: Adversarial training for textual entailment with knowledge-guided examples , in: I. Gurevych, Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, Association for Computational Linguistics, 2018, pp. 2418–2428 · 2018
Earlier work this paper cites.
C. Coulombe, Text data augmentation made simple by leveraging NLP cloud apis , arXiv abs/1812.04718 (2018) · 2018
Earlier work this paper cites.
A. W. Yu, D. Dohan, M. Luong, R. Zhao, K. Chen, M. Norouzi, Q. V. Le, Qanet: Combining local convolution with global self-attention for reading comprehension , in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, 2018. URL https://openreview.net/forum?id=B14TlG-RW
2018
Earlier work this paper cites.
S. T. Aroyehun, A. F. Gelbukh, Aggression detection in social media: Using deep neural networks, data augmentation, and pseudo labeling , in: R. Kumar, A. K. Ojha, M. Zampieri, S. Malmasi (Eds.), Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying, TRAC@COLING 2018, Santa Fe, New Mexico, USA, August 25, 2018, Association for Computational Linguistics, 2018, pp. 90–97. URL https://aclanthology.org/W18-4411/
2018
Earlier work this paper cites.
doi:10.18653/v1/d18-1100
X. Wang, H. Pham, Z. Dai, G. Neubig, Switchout: an efficient data augmentation algorithm for neural machine translation , in: E. Riloff, D. Chiang, J. Hockenmaier, J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, Association for Computational Linguistics, 2018, pp. 856–861 · 2018
Earlier work this paper cites.
H. Zhang, M. Cissé, Y. N. Dauphin, D. Lopez-Paz, mixup: Beyond empirical risk minimization , in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, 2018. URL https://openreview.net/forum?id=r1Ddp1-Rb
2018
Earlier work this paper cites.
J. Risch, R. Krestel, Aggression identification using deep learning and data augmentation , in: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), Association for Computational Linguistics, Santa Fe, New Mexico, USA, 2018, pp. 150–158. URL https://aclanthology.org/W18-4418
2018
Earlier work this paper cites.
doi:10.18653/v1/W18-5708
W. Du, A. Black, Data augmentation for neural online chats response selection , in: Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, Association for Computational Linguistics, Brussels, Belgium, 2018, pp. 52–58 · 2018
Earlier work this paper cites.
doi:10.1186/s40537-019-0197-0
C. Shorten, T. M. Khoshgoftaar, A survey on image data augmentation for deep learning , J. Big Data 6 (2019) 60 · 2019
Earlier work this paper cites.
doi:10.18653/v1/D19-1670
J. W. Wei, K. Zou, EDA: easy data augmentation techniques for boosting performance on text classification tasks , in: K. Inui, J. Jiang, V. Ng, X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, Association for Computational Linguistics, 2019, pp. 6381–6387 · 2019
Earlier work this paper cites.
F. M. Luque, Atalaya at TASS 2019: Data augmentation and robust embeddings for sentiment analysis , in: M. Á. G. Cumbreras, J. Gonzalo, E. M. Cámara, R. Martínez-Unanue, P. Rosso, J. Carrillo-de-Albornoz, S. Montalvo, L. Chiruzzo, S. Collovini, Y. Gutiérrez, S. M. J. Zafra, M. Krallinger, M. Montes-y-Gómez, R. Ortega-Bueno, A. Rosá (Eds.), Proceedings of the Iberian Languages Evaluation Forum co-located with 35th Conference of the Spanish Society for Natural Language Processing, IberLEF@SEPLN 2019, Bilbao, Spain, September 24th, 2019, Vol. 2421 of CEUR Workshop Proceedings, CEUR-WS.org, 2019, pp. 561–570. URL http://ceur-ws.org/Vol-2421/TASS_paper_1.pdf
2019
Earlier work this paper cites.
doi:10.1007/978-3-030-36204-1\_19
G. Yan, Y. Li, S. Zhang, Z. Chen, Data augmentation for deep learning of judgment documents , in: Z. Cui, J. Pan, S. Zhang, L. Xiao, J. Yang (Eds.), Intelligence Science and Big Data Engineering. Big Data and Machine Learning - 9th International Conference, IScIDE 2019, Nanjing, China, October 17-20, 2019, Proceedings, Part II, Vol. 11936 of Lecture Notes in Computer Science, Springer, 2019, pp. 232–242 · 2019
Earlier work this paper cites.
doi:10.1109/ACCESS.2019.2960263
S. Yu, J. Yang, D. Liu, R. Li, Y. Zhang, S. Zhao, Hierarchical data augmentation and the application in text classification , IEEE Access 7 (2019) 185476–185485 · 2019
Earlier work this paper cites.
X. Wu, S. Lv, L. Zang, J. Han, S. Hu, Conditional bert contextual augmentation, in: ICCS, 2019
2019
Earlier work this paper cites.
doi:10.1609/aaai.v33i01.33017402
K. M. Yoo, Y. Shin, S. Lee, Data augmentation for spoken language understanding via joint variational generation , in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, 2019, pp. 7402–7409 · 2019
Cited alongside, same era.
doi:10.18653/v1/P19-1425
Y. Cheng, L. Jiang, W. Macherey, Robust neural machine translation with doubly adversarial inputs , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 4324–4333 · 2019
Cited alongside, same era.
Z. Hu, B. Tan, R. Salakhutdinov, T. M. Mitchell, E. P. Xing, Learning data manipulation for augmentation and weighting , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 15738–15749. URL https://proceedings.neurips.cc/paper/2019/hash/671f0311e2754fcdd37f70a8550379bc-Abstract.html
2019
Cited alongside, same era.
doi:10.18653/v1/2020.coling-main.542
Z. Guo, Z. Liu, Z. Ling, S. Wang, L. Jin, Y. Li, Text classification by contrastive learning and cross-lingual data augmentation for Alzheimer’s disease detection , in: Proceedings of the 28th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Barcelona, Spain (Online), 2020, pp. 6161–6171 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.coling-main.200
Z. Wan, X. Wan, W. Wang, Improving grammatical error correction with data augmentation by editing latent representation , in: D. Scott, N. Bel, C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, International Committee on Computational Linguistics, 2020, pp. 2202–2212 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.emnlp-main.274
K. M. Yoo, H. Lee, F. Dernoncourt, T. Bui, W. Chang, S. Lee, Variational hierarchical dialog autoencoder for dialog state tracking data augmentation , in: B. Webber, T. Cohn, Y. He, Y. Liu (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, 2020, pp. 3406–3425 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.emnlp-main.277
R. Zhang, Y. Zheng, J. Shao, X. Mao, Y. Xi, M. Huang, Dialogue distillation: Open-domain dialogue augmentation using unpaired data , in: B. Webber, T. Cohn, Y. He, Y. Liu (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, 2020, pp. 3449–3460 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Longpre, Y. Lu, Z. Tu, C. DuBois, An exploration of data augmentation and sampling techniques for domain-agnostic question answering , in: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, Association for Computational Linguistics, Hong Kong, China, 2019, pp. 220–227 · 2019
Cited alongside, same era.
doi:10.18653/v1/D19-1375
Z. Zhao, S. Zhu, K. Yu, Data augmentation with atomic templates for spoken language understanding , 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), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 3637–3643 · 2019
Cited alongside, same era.
doi:10.18653/v1/N19-1363
A. Kumar, S. Bhattamishra, M. Bhandari, P. Talukdar, Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation , 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), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 3609–3619 · 2019
Cited alongside, same era.
J. Li, L. Qiu, B. Tang, D. Chen, D. Zhao, R. Yan, Insufficient data can also rock! learning to converse using smaller data with augmentation, in: AAAI, 2019
2019
Cited alongside, same era.
doi:10.18653/v1/N19-1418
T. Bergmanis, S. Goldwater, Data augmentation for context-sensitive neural lemmatization using inflection tables and raw text , 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), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 4119–4128 · 2019
Cited alongside, same era.
doi:10.18653/v1/p19-1161
R. Zmigrod, S. J. Mielke, H. M. Wallach, R. Cotterell, Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology , in: A. Korhonen, D. R. Traum, L. Màrquez (Eds.), Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Association for Computational Linguistics, 2019, pp. 1651–1661 · 2019
Cited alongside, same era.
doi:10.18653/v1/D19-1132
T. Niu, M. Bansal, Automatically learning data augmentation policies for dialogue 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), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 1317–1323 · 2019
Cited alongside, same era.
doi:10.18653/v1/P19-1555
F. Gao, J. Zhu, L. Wu, Y. Xia, T. Qin, X. Cheng, W. Zhou, T.-Y. Liu, Soft contextual data augmentation for neural machine translation , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 5539–5544 · 2019
Cited alongside, same era.
doi:10.18653/v1/P19-1579
M. Xia, X. Kong, A. Anastasopoulos, G. Neubig, Generalized data augmentation for low-resource translation , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 5786–5796 · 2019
Cited alongside, same era.
Later among the works it cites.
doi:10.18653/v1/2020.acl-main.499
A. Asai, H. Hajishirzi, Logic-guided data augmentation and regularization for consistent question answering , in: D. Jurafsky, J. Chai, N. Schluter, J. R. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Association for Computational Linguistics, 2020, pp. 5642–5650 · 2020
Later among the works it cites.
doi:10.1007/s11192-020-03351-6
Y. Zhou, F. Dong, Y. Liu, Z. Li, J. Du, L. Zhang, Forecasting emerging technologies using data augmentation and deep learning , Scientometrics 123 (1) (2020) 1–29 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.coling-main.399
L. Yao, B. Yang, H. Zhang, B. Chen, W. Luo, Domain transfer based data augmentation for neural query translation , in: D. Scott, N. Bel, C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, International Committee on Computational Linguistics, 2020, pp. 4521–4533 · 2020
Later among the works it cites.
doi:10.24963/ijcai.2020/496
G. Chen, Y. Chen, Y. Wang, V. O. K. Li, Lexical-constraint-aware neural machine translation via data augmentation , in: C. Bessiere (Ed.), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, ijcai.org, 2020, pp. 3587–3593 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.coling-main.557
R. Cao, R. K. Lee, Hategan: Adversarial generative-based data augmentation for hate speech detection , in: D. Scott, N. Bel, C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, International Committee on Computational Linguistics, 2020, pp. 6327–6338 · 2020
Later among the works it cites.
doi:10.18653/v1/2020.emnlp-demos.16
J. Morris, E. Lifland, J. Y. Yoo, J. Grigsby, D. Jin, Y. Qi, TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP , in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Association for Computational Linguistics, Online, 2020, pp. 119–126 · 2020
Later among the works it cites.
S. Chen, E. Dobriban, J. Lee, A group-theoretic framework for data augmentation, Advances in neural information processing systems 33 (2020) 21321–21333
2020
Later among the works it cites.
S. Wu, H. Zhang, G. Valiant, C. Ré, On the generalization effects of linear transformations in data augmentation, in: International Conference on Machine Learning, PMLR, 2020, pp. 10410–10420
2020
Later among the works it cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu, Exploring the limits of transfer learning with a unified text-to-text transformer , J. Mach. Learn. Res. 21 (2020) 140:1–140:67. URL http://jmlr.org/papers/v21/20-074.html
2020
Later among the works it cites.
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, D. Amodei, Language models are few-shot learners , in: H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin (Eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
2020
Later among the works it cites.
doi:10.18653/v1/2021.findings-acl.84
S. Y. Feng, V. Gangal, J. Wei, S. Chandar, S. Vosoughi, T. Mitamura, E. Hovy, A survey of data augmentation approaches for NLP , in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Association for Computational Linguistics, Online, 2021, pp. 968–988 · 2021
Closest in time.
M. Bayer, M. Kaufhold, C. Reuter, A survey on data augmentation for text classification , CoRR abs/2107.03158 (2021) · 2021
Closest in time.
M. A. Bornea, L. Pan, S. Rosenthal, R. Florian, A. Sil, Multilingual transfer learning for QA using translation as data augmentation , in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, 2021, pp. 12583–12591. URL https://ojs.aaai.org/index.php/AAAI/article/view/17491
2021
Closest in time.
doi:10.18653/v1/2021.naacl-main.28
N. Thakur, N. Reimers, J. Daxenberger, I. Gurevych, Augmented SBERT: data augmentation method for improving bi-encoders for pairwise sentence scoring tasks , in: K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tür, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Association for Computational Linguistics, 2021, pp. 296–310 · 2021
Closest in time.
doi:10.18653/v1/2021.naacl-main.426
J. Du, E. Grave, B. Gunel, V. Chaudhary, O. Celebi, M. Auli, V. Stoyanov, A. Conneau, Self-training improves pre-training for natural language understanding , in: K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tür, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Association for Computational Linguistics, 2021, pp. 5408–5418 · 2021
Closest in time.
D. Lowell, B. E. Howard, Z. C. Lipton, B. C. Wallace, Unsupervised data augmentation with naive augmentation and without unlabeled data , in: M. Moens, X. Huang, L. Specia, S. W. Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, 2021, pp. 4992–5001. URL https://aclanthology.org/2021.emnlp-main.408
2021
Closest in time.
doi:10.18653/v1/2021.naacl-main.57
A. R. Fabbri, S. Han, H. Li, H. Li, M. Ghazvininejad, S. R. Joty, D. R. Radev, Y. Mehdad, Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation , in: K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tür, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Association for Computational Linguistics, 2021, pp. 704–717 · 2021
Closest in time.
doi:10.1007/978-3-030-86967-0\_12
T. Nugent, N. Stelea, J. L. Leidner, Detecting environmental, social and governance (ESG) topics using domain-specific language models and data augmentation, in: T. Andreasen, G. D. Tré, J. Kacprzyk, H. L. Larsen, G. Bordogna, S. Zadrozny (Eds.), Proceedings of the 14th International Conference on Flexible Query Answering Systems (FQAS 2021), Bratislava, Slovakia, September 19-24, 2021, Vol. 12871 of Lecture Notes in Computer Science, Springer, 2021, pp. 157–169 · 2021
Closest in time.
Y. Qu, D. Shen, Y. Shen, S. Sajeev, W. Chen, J. Han, Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding , in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, 2021. URL https://openreview.net/forum?id=Ozk9MrX1hvA
2021
Closest in time.
Y. Hou, S. Chen, W. Che, C. Chen, T. Liu, C2c-genda: Cluster-to-cluster generation for data augmentation of slot filling , in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, 2021, pp. 13027–13035. URL https://ojs.aaai.org/index.php/AAAI/article/view/17540
2021
Closest in time.
T. Kober, J. Weeds, L. Bertolini, D. J. Weir, Data augmentation for hypernymy detection , in: P. Merlo, J. Tiedemann, R. Tsarfaty (Eds.), Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, Association for Computational Linguistics, 2021, pp. 1034–1048. URL https://aclanthology.org/2021.eacl-main.89/
2021
Closest in time.
doi:10.1007/978-3-030-77961-0\_59
X. Song, L. Zang, S. Hu, Data augmentation for copy-mechanism in dialogue state tracking , in: M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, P. M. A. Sloot (Eds.), Computational Science - ICCS 2021 - 21st International Conference, Krakow, Poland, June 16-18, 2021, Proceedings, Part I, Vol. 12742 of Lecture Notes in Computer Science, Springer, 2021, pp. 736–749 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-short.35
Y. Yang, N. Jin, K. Lin, M. Guo, D. Cer, Neural retrieval for question answering with cross-attention supervised data augmentation , in: C. Zong, F. Xia, W. Li, R. Navigli (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 2: Short Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, 2021, pp. 263–268 · 2021
Closest in time.
Che,Wanxiang and Guo,Jiang and Cui,Yiming, Natural language processing: methods based on pre-trained models, Electronic Industry Press, 2021
2021
Closest in time.
doi:10.18653/v1/2021.acl-long.96
V. Kovatchev, P. Smith, M. G. Lee, R. T. Devine, Can vectors read minds better than experts? comparing data augmentation strategies for the automated scoring of children’s mindreading ability , in: C. Zong, F. Xia, W. Li, R. Navigli (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, 2021, pp. 1196–1206 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.154
M. S. Bari, T. Mohiuddin, S. Joty, UXLA: A robust unsupervised data augmentation framework for zero-resource cross-lingual NLP , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 1978–1992 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.338
J. Chen, D. Shen, W. Chen, D. Yang, HiddenCut: Simple data augmentation for natural language understanding with better generalizability , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 4380–4390 · 2021
Closest in time.
doi:10.18653/v1/2021.findings-acl.307
H. Shi, K. Livescu, K. Gimpel, Substructure substitution: Structured data augmentation for NLP , in: C. Zong, F. Xia, W. Li, R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021, Vol. ACL/IJCNLP 2021 of Findings of ACL, Association for Computational Linguistics, 2021, pp. 3494–3508 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.300
Y. Chen, C. Kedzie, S. Nair, P. Galuscakova, R. Zhang, D. Oard, K. McKeown, Cross-language sentence selection via data augmentation and rationale training , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 3881–3895 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.363
Z. Jiang, J. Han, B. Sisman, X. L. Dong, CoRI: Collective relation integration with data augmentation for open information extraction , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 4706–4716 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.453
L. Liu, B. Ding, L. Bing, S. Joty, L. Si, C. Miao, MulDA: A multilingual data augmentation framework for low-resource cross-lingual NER , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 5834–5846 · 2021
Closest in time.
I. Staliunaite, P. J. Gorinski, I. Iacobacci, Improving commonsense causal reasoning by adversarial training and data augmentation , in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, 2021, pp. 13834–13842. URL https://ojs.aaai.org/index.php/AAAI/article/view/17630
2021
Closest in time.
doi:10.18653/v1/2021.acl-long.401
X. Dong, Y. Zhu, Z. Fu, D. Xu, G. de Melo, Data augmentation with adversarial training for cross-lingual NLI , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 5158–5167 · 2021
Closest in time.
A. Riabi, T. Scialom, R. Keraron, B. Sagot, D. Seddah, J. Staiano, Synthetic data augmentation for zero-shot cross-lingual question answering , in: M. Moens, X. Huang, L. Specia, S. W. Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, 2021, pp. 7016–7030. URL https://aclanthology.org/2021.emnlp-main.562
2021
Closest in time.
doi:10.18653/v1/2021.acl-long.95
X. Xu, G. Wang, Y.-B. Kim, S. Lee, AugNLG: Few-shot natural language generation using self-trained data augmentation , in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online, 2021, pp. 1183–1195 · 2021
Closest in time.
doi:10.18653/v1/2021.findings-acl.137
C. Si, Z. Zhang, F. Qi, Z. Liu, Y. Wang, Q. Liu, M. Sun, Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning , in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Association for Computational Linguistics, Online, 2021, pp. 1569–1576 · 2021
Closest in time.
doi:10.18653/v1/2021.acl-long.264
B. Zheng, L. Dong, S. Huang, W. Wang, Z. Chi, S. Singhal, W. Che, T. Liu, X. Song, F. Wei, Consistency regularization for cross-lingual fine-tuning , in: C. Zong, F. Xia, W. Li, R. Navigli (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Association for Computational Linguistics, 2021, pp. 3403–3417 · 2021
Closest in time.
D. Singh, S. Reddy, W. Hamilton, C. Dyer, D. Yogatama, End-to-end training of multi-document reader and retriever for open-domain question answering, Advances in Neural Information Processing Systems 34 (2021) 25968–25981
2021
Closest in time.
D. Yogatama, C. de Masson d’Autume, L. Kong, Adaptive semiparametric language models, Transactions of the Association for Computational Linguistics 9 (2021) 362–373
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
doi:10.18653/v1/2022.acl-long.592
J. Zhou, Y. Zheng, J. Tang, L. Jian, Z. Yang, FlipDA: Effective and robust data augmentation for few-shot learning , in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland, 2022, pp. 8646–8665 · 2022
Closest in time.
2022
Closest in time.
doi:10.18653/v1/n18-2072
S. Kobayashi, Contextual augmentation: Data augmentation by words with paradigmatic relations , in: M. A. Walker, H. Ji, A. Stent (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), Association for Computational Linguistics, 2018, pp. 452–457 · 2072
Closest in time.
doi:10.18653/v1/P17-2090
M. Fadaee, A. Bisazza, C. Monz, Data augmentation for low-resource neural machine translation , in: R. Barzilay, M. Kan (Eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 2: Short Papers, Association for Computational Linguistics, 2017, pp. 567–573 · 2090
Closest in time.