Advances and Challenges in Few-Shot Learning for Natural Language Processing: A Pilot Study
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1260-1_4
Reference13 articles.
1. Müller T, Pérez-Torró G, Basile A, Franco-Salvador M (2022) Active few-shot learning with FASL. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence. Lecture notes in bioinformatics), vol 13286 LNCS, pp 98–110
2. Matindife L, Sun Y, Wang Z (2022) Few-shot learning for image-based nonintrusive appliance signal recognition. Comput Intell Neurosci 2022
3. Duan R, Li D, Tong Q, Yang T, Liu X, Liu X (2021) A survey of few-shot learning: an effective method for intrusion detection. Secure Commun Netw 2021
4. Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few instances: a survey on few-shot learning. ACM Comput Surv 53(3):1–34
5. Zhang Z, Takanobu R, Zhu Q, Huang ML, Zhu XY (2020) Recent advances and challenges in task-oriented dialog systems. Sci China Technol Sci. 63(10):2011–2027
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