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Few-shot natural language processing (NLP) refers to NLP tasks that are accompanied with merely a handful of labeled examples.
Learning to few-shot learn across diverse natural language classification tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2019 · 1911
Earlier work this paper cites.
Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Y. Pratt. 1998 · 1998
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi. 2002 · 2002
Earlier work this paper cites.
Learning to learn to disambiguate: Meta-learning for few-shot word sense disambiguation
Nithin Holla, Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2020 · 2004
Earlier work this paper cites.
Meta-learning in neural networks: A survey
Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J. Storkey. 2020 · 2004
Earlier work this paper cites.
Knowledge guided metric learning for few-shot text classification
Dianbo Sui, Yubo Chen, Binjie Mao, Delai Qiu, Kang Liu, and Jun Zhao. 2020 · 2004
Earlier work this paper cites.
CG-BERT: conditional text generation with BERT for generalized few-shot intent detection
Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, and Philip S. Yu. 2020 · 2004
Earlier work this paper cites.
Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015 · 2015
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2016 · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Cited alongside, same era.
Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018 · 2018
Cited alongside, same era.
Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018 · 2018
Cited alongside, same era.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Cited alongside, same era.
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
Later among the works it cites.
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.
Investigating meta-learning algorithms for low-resource natural language understanding tasks
Zi-Yi Dou, Keyi Yu, and Antonios Anastasopoulos. 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.
An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
Later among the works it cites.
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Attentive task-agnostic meta-learning for few-shot text classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, and Stan Matwin. 2018 · 2018
Cited alongside, same era.
SciTaiL: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. 2018 · 2018
Cited alongside, same era.
Joaquin Vanschoren. 2018 · 2018
Cited alongside, same era.
Zero-shot user intent detection via capsule neural networks
Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, and Philip S. Yu. 2018 · 2018
Cited alongside, same era.
Hybrid attention-based prototypical networks for noisy few-shot relation classification
Tianyu Gao, Xu Han, Zhiyuan Liu, and Maosong Sun. 2019a
Cited in the paper.
Hierarchical attention prototypical networks for few-shot text classification
Shengli Sun, Qingfeng Sun, Kevin Zhou, and Tengchao Lv. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Meta-learning with dynamic-memory-based prototypical network for few-shot event detection
Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, and Huajun Chen. 2020 · 2020
Closest in time.
Exploiting the matching information in the support set for few shot event classification
Viet Dac Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2020 · 2020
Closest in time.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. 2020 · 2020
Closest in time.