FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

Bert for joint intent classification and slot filling

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Neural snowball for few-shot relation learning

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Tianyu Gao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2019a · 1908

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Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations

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Xin Lv, Yuxian Gu, Xu Han, Lei Hou, Juanzi Li, and Zhiyuan Liu. 2019 · 1908

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Fewrel 2.0: Towards more challenging few-shot relation classification

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Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019b · 1910

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Learning from one example through shared densities on transforms

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Conditional random fields: Probabilistic models for segmenting and labeling sequence data

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Accurate unlexicalized parsing

Dan Klein and Christopher D Manning. 2003 · 2003

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One-shot learning of object categories

Li Fei-Fei, Rob Fergus, and Pietro Perona. 2006 · 2006

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Adam: A method for stochastic optimization

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Diederik P. Kingma and Jimmy Ba. 2015 · 2015

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Human-level concept learning through probabilistic program induction

Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015 · 2015

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Matching networks for one shot learning

Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2016 · 2016

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Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging

Nils Reimers and Iryna Gurevych. 2017 · 2017

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Prototypical networks for few-shot learning

Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017

Cited alongside, same era.

Slot-gated modeling for joint slot filling and intent prediction

Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, and Yun-Nung Chen. 2018 · 2018

Cited alongside, same era.

Universal language model fine-tuning for text classification

BERT: pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

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Induction networks for few-shot text classification

Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, and Jian Sun. 2019 · 2019

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Hierarchical attention prototypical networks for few-shot text classification

Shengli Sun, Qingfeng Sun, Kevin Zhou, and Tengchao Lv. 2019 · 2019

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Multi-level matching and aggregation network for few-shot relation classification

Zhi-Xiu Ye and Zhen-Hua Ling. 2019 · 2019

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Tapnet: Neural network augmented with task-adaptive projection for few-shot learning

Sung Whan Yoon, Jun Seo, and Jaekyun Moon. 2019 · 2019

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Few-shot text classification with distributional signatures