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This document discusses meta-learning approaches for few-shot natural language processing. It explains that meta-learning, or few-shot learning, can help address issues with limited data by learning across many previous tasks to enable fast learning of new tasks. The document provides links to resources on benchmark datasets and papers related to meta-learning and few-shot learning for NLP tasks.
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Updated with the edits we last spoke BhavanaShort story slides
- 1. Learn to Learn: A survey on Meta-learning for Few-shot Natural Language Processing Paper by Wenpeng Yin Presented by- Jumana Nadir
- 2. Less Data
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- 4. Meta-learning or few-shot learning offers a potential solution to these problems: by learning to learn across data from many previous tasks. Few-shot meta-learning algorithms can discover the structure among tasks to enable fast learning of new tasks.
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- 19. 則 則 https://deepai.org/publication/meta-learning-for-few-shot-natural-language-processing-a-survey 則 https://paperswithcode.com/paper/siamese-neural-networks-for-one-shot-image 則 https://arxiv.org/abs/1606.04080 則 https://arxiv.org/abs/1703.05175 則 https://arxiv.org/abs/1711.06025