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

Volumn , Issue , 2019, Pages 6382-6388

  Wei, Jason ^a,b   Zou, Kai ^c  

^a Tysons Corner   (United States)

^b Dartmouth College   (United States)

^c GEORGETOWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); NATURAL LANGUAGE PROCESSING SYSTEMS; RECURRENT NEURAL NETWORKS;

DATA AUGMENTATION; PRACTICAL USE; RANDOM INSERTIONS; TEXT CLASSIFICATION; TRAINING SETS;

TEXT PROCESSING;

EID: 85084311862     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Xiaodong Cui, Vaibhava Goel, and Brian Kingsbury. 2015. Data augmentation for deep neural network acoustic modeling. IEEE/ACM Trans. Audio, Speech and Lang. Proc., 23(9):1469-1477.
2. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805.
3. Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017. Data augmentation for low-resource neural machine translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 567-573. Association for Computational Linguistics.
4. Murthy Ganapathibhotla and Bing Liu. 2008. Mining opinions in comparative sentences. In Proceedings of the 22Nd International Conference on Computational Linguistics - Volume 1, COLING'08, pages 241-248, Stroudsburg, PA, USA. Association for Computational Linguistics.
5. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'04, pages 168-177, New York, NY, USA. ACM.
6. Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017. Toward controlled generation of text. In ICML.
7. Kushal Kafle, Mohammed Yousefhussien, and Christopher Kanan. 2017. Data augmentation for visual question answering. In Proceedings of the 10th International Conference on Natural Language Generation, pages 198-202. Association for Computational Linguistics.
8. Yoon Kim. 2014. Convolutional neural networks for sentence classification. CoRR, abs/1408.5882.
9. Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur. 2015. Audio augmentation for speech recognition. In INTERSPEECH.
10. Sosuke Kobayashi. 2018. Contextual augmentation: Data augmentation by words with paradigmatic relations. In NAACL-HLT.
11. Oleksandr Kolomiyets, Steven Bethard, and Marie-Francine Moens. 2011. Model-portability experiments for textual temporal analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers - Volume 2, HLT'11, pages 271-276, Stroudsburg, PA, USA. Association for Computational Linguistics.
12. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Commun. ACM, 60(6):84-90.
13. Xin Li and Dan Roth. 2002. Learning question classifiers. In Proceedings of the 19th International Conference on Computational Linguistics - Volume 1, COLING'02, pages 1-7, Stroudsburg, PA, USA. Association for Computational Linguistics.
14. Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16, pages 2873-2879. AAAI Press.
15. Qian Liu, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2015. Automated rule selection for aspect extraction in opinion mining. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI'15, pages 1291-1297. AAAI Press.
16. George A. Miller. 1995. Wordnet: A lexical database for english. Commun. ACM, 38(11):39-41.
17. Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL'04, Stroudsburg, PA, USA. Association for Computational Linguistics.
18. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543.
19. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. CoRR, abs/1802.05365.
20. David Rolnick, Andreas Veit, Serge J. Belongie, and Nir Shavit. 2017. Deep learning is robust to massive label noise. CoRR, abs/1705.10694.
21. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. 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), pages 86-96. Association for Computational Linguistics.
22. Sam Shleifer. 2019. Low resource text classification with ulmfit and backtranslation. CoRR, abs/1903.09244.
23. Miikka Silfverberg, Adam Wiemerslage, Ling Liu, and Lingshuang Jack Mao. 2017. Data augmentation for morphological reinflection. In Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, pages 90-99. Association for Computational Linguistics.
24. Patrice Simard, Yann LeCun, John S. Denker, and Bernard Victorri. 1998. Transformation invariance in pattern recognition-tangent distance and tangent propagation. In Neural Networks: Tricks of the Trade, This Book is an Outgrowth of a 1996 NIPS Workshop, pages 239-27, London, UK, UK. Springer-Verlag.
25. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng, and Christopher Potts. 2013. Parsing With Compositional Vector Grammars. In EMNLP.
26. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2014. Going deeper with convolutions. CoRR, abs/1409.4842.
27. Duyu Tang, Bing Qin, and Ting Liu. 2015. Document modeling with gated recurrent neural network for sentiment classification. pages 1422-1432.
28. Simon Tong and Daphne Koller. 2002. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res., 2:45-66.
29. Laurens Van Der Maaten. 2014. Accelerating t-sne using tree-based algorithms. J. Mach. Learn. Res., 15(1):3221-3245.
30. William Yang Wang and Diyi Yang. 2015. 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, pages 2557-2563. Association for Computational Linguistics.
31. Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Levy, Aiming Nie, Dan Jurafsky, and Andrew Y. Ng. 2017. Data noising as smoothing in neural network language models.
32. Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018. Qanet: Combining local convolution with global self-attention for reading comprehension. CoRR, abs/1804.09541.
33. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS'15, pages 649-657, Cambridge, MA, USA. MIT Press.