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Recent advances in large pretrained language models have increased attention to zero-shot text classification.
Language models are few-shot learners
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Huggingface’s transformers: State-of-the-art natural language processing
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Active online classification via information maximization
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
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Hidden factors and hidden topics: understanding rating dimensions with review text
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Universality versus cultural specificity of three emotion domains: Some evidence based on the cascading model of emotional intelligence
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Isaac Triguero, Salvador García, and Francisco Herrera. 2015 · 2015
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Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
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Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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A survey on semi-supervised learning
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020 · 2020
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Zero-shot text classification via reinforced self-training
Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, and Huajun Chen. 2020 · 2020
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Muppet: Massive multi-task representations with pre-finetuning
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Probabilistic ensembles of zero- and few-shot learning models for emotion classification
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Flex: Unifying evaluation for few-shot nlp
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019 · 2019
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Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, BVK Kumar, and Jinsong Wang. 2019 · 2019
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Zero-shot learning in modern NLP
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GoEmotions: A dataset of fine-grained emotions
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Text classification using label names only: A language model self-training approach
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Uncertainty-aware self-training for few-shot text classification
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Self-training improves pre-training for natural language understanding
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Weakly-supervised text classification based on keyword graph
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022 · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022 · 2022
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Prompt consistency for zero-shot task generalization
Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022 · 2022
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