Understand
The training of deep-learning-based text classification models relies heavily on a huge amount of annotation data, which is difficult to obtain.
- When the labeled data is scarce, models tend to struggle to achieve satisfactory performance.
- However, human beings can distinguish new categories very efficiently with few examples.
- This is mainly due to the fact that human beings can leverage knowledge obtained from relevant tasks.
Built on
Few-shot text classification with distributional signatures
Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay. 2019 · 1908
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Improving few-shot text classification via pretrained language representations
Ningyu Zhang, Zhanlin Sun, Shumin Deng, Jiaoyan Chen, and Huajun Chen. 2019 · 1908
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Toward an architecture for never-ending language learning
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka, and Tom M Mitchell. 2010 · 2010
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
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Similar
David Ha, Andrew Dai, and Quoc V Le. 2016 · 2016
Cited alongside, same era.
Dynamic filter networks
Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool. 2016 · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Cited alongside, same era.
Then
Openke: An open toolkit for knowledge embedding
Xu Han, Shulin Cao, Lv Xin, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. 2018 · 2018
Later among the works it cites.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018 · 2018
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Diverse few-shot text classification with multiple metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Induction networks for few-shot text classification
Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, and Jian Sun. 2019 · 2019
Later among the works it cites.
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