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Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning.
Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 1905
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Mining and summarizing customer reviews
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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A simple but tough-to-beat data augmentation approach for natural language understanding and generation
Dinghan Shen, Mingzhi Zheng, Yelong Shen, Yanru Qu, and Weizhu Chen. 2020 · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Deep headline generation for clickbait detection
Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu. 2018 · 2018
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Nlp augmentation
Edward Ma. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 2019
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Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
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Effective few-shot classification with transfer learning
Aakriti Gupta, Kapil Thadani, and Neil O’Hare. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Data boost: Text data augmentation through reinforcement learning guided conditional generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, and Soroush Vosoughi. 2020 · 2020
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Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2020
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Uncertainty-aware self-training for few-shot text classification
Subhabrata Mukherjee and Ahmed Hassan Awadallah. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Learning with weak supervision for email intent detection
Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, and Susan T. Dumais. 2020 · 2020
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Planning and generating natural and diverse disfluent texts as augmentation for disfluency detection
Jingfeng Yang, Diyi Yang, and Zhaoran Ma. 2020a · 2020
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Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020b · 2020
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Meta-learning for few-shot natural language processing: A survey
Wenpeng Yin. 2020 · 2020
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PEGASUS: pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020a · 2020
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SeqMix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020b · 2020
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A survey on data augmentation for text classification
Revisiting few-sample BERT fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, and Yoav Artzi. 2021 · 2021
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Text transformations in contrastive self-supervised learning: A review
Amrita Bhattacharjee, Mansooreh Karami, and Huan Liu. 2022 · 2022
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A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability
Chengtai Cao, Fan Zhou, Yurou Dai, and Jianping Wang. 2022 · 2022
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Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022b · 2022
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Rethinking data augmentation in text-to-text paradigm
Yanan Chen and Yang Liu. 2022 · 2022
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PPT: Pre-trained prompt tuning for few-shot learning
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Markus Bayer, Marc-André Kaufhold, and Christian Reuter. 2021 · 2021
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An empirical survey of data augmentation for limited data learning in nlp
Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal, and Diyi Yang. 2021 · 2021
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Salnet: Semi-supervised few-shot text classification with attention-based lexicon construction
Ju-Hyoung Lee, Sang-Ki Ko, and Yo-Sub Han. 2021 · 2021
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Data augmentation approaches in natural language processing: A survey
Bohan Li, Yutai Hou, and Wanxiang Che. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022 · 2022
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Prompt gating: A parameter efficient tuning method for zero-shot multi-source translation
Xuancheng Huang, Zijun Liu, Peng Li, Maosong Sun, and Yang Liu. 2022 · 2022
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DropMix: A textual data augmentation combining dropout with mixup
Fanshuang Kong, Richong Zhang, Xiaohui Guo, Samuel Mensah, and Yongyi Mao. 2022 · 2022
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Learning to transfer prompts for text generation
Junyi Li, Tianyi Tang, Jian-Yun Nie, Ji-Rong Wen, and Xin Zhao. 2022 · 2022
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Zero-shot rumor detection with propagation structure via prompt learning
Hongzhan Lin, Pengyao Yi, Jing Ma, Haiyun Jiang, Ziyang Luo, Shuming Shi, and Ruifang Liu. 2022 · 2022
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022
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CIAug: Equipping interpolative augmentation with curriculum learning
Ramit Sawhney, Ritesh Soun, Shrey Pandit, Megh Thakkar, Sarvagya Malaviya, and Yuval Pinter. 2022 · 2022
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GRASP: Guiding model with RelAtional semantics using prompt for dialogue relation extraction
Junyoung Son, Jinsung Kim, Jungwoo Lim, and Heuiseok Lim. 2022 · 2022
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MSP: Multi-stage prompting for making pre-trained language models better translators
Zhixing Tan, Xiangwen Zhang, Shuo Wang, and Yang Liu. 2022 · 2022
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KECP: Knowledge enhanced contrastive prompting for few-shot extractive question answering
Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, and Ming Gao. 2022a · 2022
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Text smoothing: Enhance various data augmentation methods on text classification tasks
Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, and Songlin Hu. 2022 · 2022
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TreeMix: Compositional constituency-based data augmentation for natural language understanding
Le Zhang, Zichao Yang, and Diyi Yang. 2022 · 2022
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EPiDA: An easy plug-in data augmentation framework for high performance text classification
Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, and Shuigeng Zhou. 2022 · 2022
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FlipDA: Effective and robust data augmentation for few-shot learning
Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, and Zhilin Yang. 2022 · 2022
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Continual prompt tuning for dialog state tracking
Qi Zhu, Bing Li, Fei Mi, Xiaoyan Zhu, and Minlie Huang. 2022 · 2022
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Meta-prompt based learning for low-resource false information detection
Yinqiu Huang, Min Gao, Jia Wang, Junwei Yin, Kai Shu, Qilin Fan, and Junhao Wen. 2023 · 2023
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Data augmentation for neural nlp
Domagoj Pluščec and Jan Šnajder. 2023 · 2023
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