Built on

H. Guo, Y. Mao, and R. Zhang, “Augmenting data with mixup for sentence classification: An empirical study,” ArXiv , 5 2019. [Online]. Available: https://arxiv.org/abs/1905.08941v1
Original
1905
Earlier work this paper cites.
D. Ho, E. Liang et al. , “Population based augmentation: Efficient learning of augmentation policy schedules,” CoRR , vol. abs/1905.05393, 2019. [Online]. Available: http://arxiv.org/abs/1905.05393
Original
1905
Earlier work this paper cites.
I. Solaiman, M. Brundage et al. , “Release strategies and the social impacts of language models,” CoRR , vol. abs/1908.09203, 2019. [Online]. Available: http://arxiv.org/abs/1908.09203
Original
1908
Earlier work this paper cites.
E. D. Cubuk, B. Zoph et al. , “Randaugment: Practical data augmentation with no separate search,” CoRR , vol. abs/1909.13719, 2019. [Online]. Available: http://arxiv.org/abs/1909.13719
Original
1909
Earlier work this paper cites.
G. A. Miller, “Wordnet: A lexical database for english,” Commun. ACM , vol. 38, no. 11, p. 39–41, nov 1995. [Online]. Available: https://doi.org/10.1145/219717.219748
1995
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N. V. Chawla, K. W. Bowyer et al. , “Smote: Synthetic minority over-sampling technique,” J. Artif. Int. Res. , vol. 16, no. 1, p. 321–357, jun 2002
2002
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P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” Proceedings of the International Conference on Document Analysis and Recognition, ICDAR , vol. 2003-January, pp. 958–963, 2003
2003
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T. B. Brown, B. Mann et al. , “Language models are few-shot learners,” CoRR , vol. abs/2005.14165, 2020. [Online]. Available: https://arxiv.org/abs/2005.14165
Original
2005
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J. X. Morris, E. Lifland et al. , “Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP,” arXiv:2005.05909v4 , pp. 119–126, 4 2020. [Online]. Available: https://arxiv.org/abs/2005.05909v4
Original
2005
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J. Ebrahimi, A. Rao et al. , “HotFlip: White-box adversarial examples for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 31–36. [Online]. Available: https://aclanthology.org/P18-2006
2006
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Z. He, Z. Wang et al. , “A survey on recent advances in sequence labeling from deep learning models,” CoRR , vol. abs/2011.06727, 2020. [Online]. Available: https://arxiv.org/abs/2011.06727
Original
2011
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R. Bhagat and E. Hovy, “What Is a Paraphrase?” Computational Linguistics , vol. 39, no. 3, pp. 463–472, 09 2013. [Online]. Available: https://doi.org/10.1162/COLI_a_00166
2013
Earlier work this paper cites.
T. Ko, V. Peddinti et al. , “Audio augmentation for speech recognition,” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH , vol. 2015-January, pp. 3586–3589, 2015
2015
Earlier work this paper cites.
W. Y. Wang and 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 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Lisbon, Portugal: Association for Computational Linguistics, Sep. 2015, pp. 2557–2563. [Online]. Available: https://aclanthology.org/D15-1306
2015
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W. Ling, C. Dyer et al. , “Finding function in form: Compositional character models for open vocabulary word representation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Lisbon, Portugal: Association for Computational Linguistics, Sep. 2015, pp. 1520–1530. [Online]. Available: https://aclanthology.org/D15-1176
2015
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 1715–1725. [Online]. Available: https://aclanthology.org/P16-1162
2016
Earlier work this paper cites.
Y. Wu, M. Schuster et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” ArXiv , vol. abs/1609.08144, 2016
Original
2016
Earlier work this paper cites.
H. Zhang, M. Cisse et al. , “mixup: Beyond empirical risk minimization,” 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings , 10 2017. [Online]. Available: https://arxiv.org/abs/1710.09412v2
Original
2017
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A. Hernández-García and P. König, “Data augmentation instead of explicit regularization,” arXiv:1806.03852 , 6 2018. [Online]. Available: https://arxiv.org/abs/1806.03852v5
Original
2018
Earlier work this paper cites.
J. H. Park, J. Shin, and P. Fung, “Reducing gender bias in abusive language detection,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 2799–2804. [Online]. Available: https://aclanthology.org/D18-1302
2018
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M. Alzantot, Y. Sharma et al. , “Generating natural language adversarial examples,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 , pp. 2890–2896, 2018. [Online]. Available: https://aclanthology.org/D18-1316
2018
Earlier work this paper cites.
C. Coulombe, “Text data augmentation made simple by leveraging nlp cloud apis,” arXiv:1812.04718 , 2018
Original
2018
Earlier work this paper cites.
G. G. Şahin and M. Steedman, “Data augmentation via dependency tree morphing for low-resource languages,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 5004–5009. [Online]. Available: https://aclanthology.org/D18-1545
2018
Earlier work this paper cites.
Y. Wang and M. Bansal, “Robust machine comprehension models via adversarial training,” CoRR , vol. abs/1804.06473, 2018. [Online]. Available: http://arxiv.org/abs/1804.06473
Original
2018
Earlier work this paper cites.
D. Cer, Y. Yang et al. , “Universal sentence encoder,” CoRR , vol. abs/1803.11175, 2018. [Online]. Available: http://arxiv.org/abs/1803.11175
Original
2018
Earlier work this paper cites.
M. Kuchnik and V. Smith, “Efficient augmentation via data subsampling,” CoRR , vol. abs/1810.05222, 2018. [Online]. Available: http://arxiv.org/abs/1810.05222
Original
2018
Earlier work this paper cites.

Similar

V. Yadav and S. Bethard, “A survey on recent advances in named entity recognition from deep learning models,” in Proceedings of the 27th International Conference on Computational Linguistics . Santa Fe, New Mexico, USA: Association for Computational Linguistics, Aug. 2018, pp. 2145–2158. [Online]. Available: https://aclanthology.org/C18-1182
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang et al. , “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) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Cited alongside, same era.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data , vol. 6, pp. 1–48, 12 2019. [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0
2019
Cited alongside, same era.

Then

S. Y. Feng, V. Gangal et al. , “A survey of data augmentation approaches for NLP,” Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , pp. 968–988, 2021. [Online]. Available: https://aclanthology.org/2021.findings-acl.84
2021
Later among the works it cites.
B. Markus, K. Marc-André, and R. Christian, “A survey on data augmentation for text classification,” ACM Computing Surveys , 7 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3544558
2021
Later among the works it cites.
M. A. Hedderich, L. Lange et al. , “A survey on recent approaches for natural language processing in low-resource scenarios,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Online: Association for Computational Linguistics, Jun. 2021, pp. 2545–2568. [Online]. Available: https://aclanthology.org/2021.naacl-main.201
2021
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2023

Data Augmentation for Neural NLP

Pluščec, Domagoj, Šnajder, Jan

Understand

Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models.

Such models are often deep learning models that require a significant amount of data to train.
Acquiring data for various machine learning problems is accompanied by high labeling costs.
Data augmentation is a low-cost approach for tackling data scarcity.

Built on

H. Guo, Y. Mao, and R. Zhang, “Augmenting data with mixup for sentence classification: An empirical study,” ArXiv , 5 2019. [Online]. Available: https://arxiv.org/abs/1905.08941v1
Original
1905
Earlier work this paper cites.
D. Ho, E. Liang et al. , “Population based augmentation: Efficient learning of augmentation policy schedules,” CoRR , vol. abs/1905.05393, 2019. [Online]. Available: http://arxiv.org/abs/1905.05393
Original
1905
Earlier work this paper cites.
I. Solaiman, M. Brundage et al. , “Release strategies and the social impacts of language models,” CoRR , vol. abs/1908.09203, 2019. [Online]. Available: http://arxiv.org/abs/1908.09203
Original
1908
Earlier work this paper cites.
E. D. Cubuk, B. Zoph et al. , “Randaugment: Practical data augmentation with no separate search,” CoRR , vol. abs/1909.13719, 2019. [Online]. Available: http://arxiv.org/abs/1909.13719
Original
1909
Earlier work this paper cites.
G. A. Miller, “Wordnet: A lexical database for english,” Commun. ACM , vol. 38, no. 11, p. 39–41, nov 1995. [Online]. Available: https://doi.org/10.1145/219717.219748
1995
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer et al. , “Smote: Synthetic minority over-sampling technique,” J. Artif. Int. Res. , vol. 16, no. 1, p. 321–357, jun 2002
2002
Earlier work this paper cites.
P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” Proceedings of the International Conference on Document Analysis and Recognition, ICDAR , vol. 2003-January, pp. 958–963, 2003
2003
Earlier work this paper cites.
T. B. Brown, B. Mann et al. , “Language models are few-shot learners,” CoRR , vol. abs/2005.14165, 2020. [Online]. Available: https://arxiv.org/abs/2005.14165
Original
2005
Earlier work this paper cites.
J. X. Morris, E. Lifland et al. , “Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP,” arXiv:2005.05909v4 , pp. 119–126, 4 2020. [Online]. Available: https://arxiv.org/abs/2005.05909v4
Original
2005
Earlier work this paper cites.
J. Ebrahimi, A. Rao et al. , “HotFlip: White-box adversarial examples for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 31–36. [Online]. Available: https://aclanthology.org/P18-2006
2006
Earlier work this paper cites.
Z. He, Z. Wang et al. , “A survey on recent advances in sequence labeling from deep learning models,” CoRR , vol. abs/2011.06727, 2020. [Online]. Available: https://arxiv.org/abs/2011.06727
Original
2011
Earlier work this paper cites.
R. Bhagat and E. Hovy, “What Is a Paraphrase?” Computational Linguistics , vol. 39, no. 3, pp. 463–472, 09 2013. [Online]. Available: https://doi.org/10.1162/COLI_a_00166
2013
Earlier work this paper cites.
T. Ko, V. Peddinti et al. , “Audio augmentation for speech recognition,” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH , vol. 2015-January, pp. 3586–3589, 2015
2015
Earlier work this paper cites.
W. Y. Wang and 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 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Lisbon, Portugal: Association for Computational Linguistics, Sep. 2015, pp. 2557–2563. [Online]. Available: https://aclanthology.org/D15-1306
2015
Earlier work this paper cites.
W. Ling, C. Dyer et al. , “Finding function in form: Compositional character models for open vocabulary word representation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Lisbon, Portugal: Association for Computational Linguistics, Sep. 2015, pp. 1520–1530. [Online]. Available: https://aclanthology.org/D15-1176
2015
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 1715–1725. [Online]. Available: https://aclanthology.org/P16-1162
2016
Earlier work this paper cites.
Y. Wu, M. Schuster et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” ArXiv , vol. abs/1609.08144, 2016
Original
2016
Earlier work this paper cites.
H. Zhang, M. Cisse et al. , “mixup: Beyond empirical risk minimization,” 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings , 10 2017. [Online]. Available: https://arxiv.org/abs/1710.09412v2
Original
2017
Earlier work this paper cites.
A. Hernández-García and P. König, “Data augmentation instead of explicit regularization,” arXiv:1806.03852 , 6 2018. [Online]. Available: https://arxiv.org/abs/1806.03852v5
Original
2018
Earlier work this paper cites.
J. H. Park, J. Shin, and P. Fung, “Reducing gender bias in abusive language detection,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 2799–2804. [Online]. Available: https://aclanthology.org/D18-1302
2018
Earlier work this paper cites.
M. Alzantot, Y. Sharma et al. , “Generating natural language adversarial examples,” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 , pp. 2890–2896, 2018. [Online]. Available: https://aclanthology.org/D18-1316
2018
Earlier work this paper cites.
C. Coulombe, “Text data augmentation made simple by leveraging nlp cloud apis,” arXiv:1812.04718 , 2018
Original
2018
Earlier work this paper cites.
G. G. Şahin and M. Steedman, “Data augmentation via dependency tree morphing for low-resource languages,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 5004–5009. [Online]. Available: https://aclanthology.org/D18-1545
2018
Earlier work this paper cites.
Y. Wang and M. Bansal, “Robust machine comprehension models via adversarial training,” CoRR , vol. abs/1804.06473, 2018. [Online]. Available: http://arxiv.org/abs/1804.06473
Original
2018
Earlier work this paper cites.
D. Cer, Y. Yang et al. , “Universal sentence encoder,” CoRR , vol. abs/1803.11175, 2018. [Online]. Available: http://arxiv.org/abs/1803.11175
Original
2018
Earlier work this paper cites.
M. Kuchnik and V. Smith, “Efficient augmentation via data subsampling,” CoRR , vol. abs/1810.05222, 2018. [Online]. Available: http://arxiv.org/abs/1810.05222
Original
2018
Earlier work this paper cites.

Similar

V. Yadav and S. Bethard, “A survey on recent advances in named entity recognition from deep learning models,” in Proceedings of the 27th International Conference on Computational Linguistics . Santa Fe, New Mexico, USA: Association for Computational Linguistics, Aug. 2018, pp. 2145–2158. [Online]. Available: https://aclanthology.org/C18-1182
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang et al. , “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) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Cited alongside, same era.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data , vol. 6, pp. 1–48, 12 2019. [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0
2019
Cited alongside, same era.

Then

S. Y. Feng, V. Gangal et al. , “A survey of data augmentation approaches for NLP,” Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , pp. 968–988, 2021. [Online]. Available: https://aclanthology.org/2021.findings-acl.84
2021
Later among the works it cites.
B. Markus, K. Marc-André, and R. Christian, “A survey on data augmentation for text classification,” ACM Computing Surveys , 7 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3544558
2021
Later among the works it cites.
M. A. Hedderich, L. Lange et al. , “A survey on recent approaches for natural language processing in low-resource scenarios,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Online: Association for Computational Linguistics, Jun. 2021, pp. 2545–2568. [Online]. Available: https://aclanthology.org/2021.naacl-main.201
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Wei and K. Zou, “Eda: Easy data augmentation techniques for boosting performance on text classification tasks,” EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference , pp. 6382–6388, 2019. [Online]. Available: https://aclanthology.org/D19-1670

2019

Cited alongside, same era.

X. Wu, S. Lv et al. , “Conditional bert contextual augmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11539 LNCS, pp. 84–95, 2019. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-22747-0_7

2019

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V. Karpukhin, O. Levy et al. , “Training on synthetic noise improves robustness to natural noise in machine translation,” in Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 42–47. [Online]. Available: https://aclanthology.org/D19-5506

2019

Cited alongside, same era.

D. Berthelot, N. Carlini et al. , MixMatch: A Holistic Approach to Semi-Supervised Learning . Red Hook, NY, USA: Curran Associates Inc., 2019

2019

Cited alongside, same era.

G. Yan, Y. Li et al. , “Data augmentation for deep learning of judgment documents,” in Intelligence Science and Big Data Engineering. Big Data and Machine Learning , Z. Cui, J. Pan et al. , Eds. Cham: Springer International Publishing, 2019, pp. 232–242

2019

Cited alongside, same era.

G. Rizos, K. Hemker, and B. Schuller, “Augment to prevent: Short-text data augmentation in deep learning for hate-speech classification,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , ser. CIKM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 991–1000. [Online]. Available: https://doi.org/10.1145/3357384.3358040

2019

Cited alongside, same era.

Y. Cheng, L. Jiang, and W. Macherey, “Robust neural machine translation with doubly adversarial inputs,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 4324–4333. [Online]. Available: https://aclanthology.org/P19-1425

2019

Cited alongside, same era.

M. Jungiewicz and A. Smywinski-Pohl, “Towards textual data augmentation for neural networks: synonyms and maximum loss,” Computer Science , vol. 20, no. 1, Mar. 2019. [Online]. Available: https://journals.agh.edu.pl/csci/article/view/3023

2019

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——, “Autoaugment: Learning augmentation policies from data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019. [Online]. Available: https://arxiv.org/pdf/1805.09501.pdf

Original

2019

Cited alongside, same era.

T.-H. Cheung and D.-Y. Yeung, “{MODALS}: Modality-agnostic automated data augmentation in the latent space,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=XjYgR6gbCEc

2021