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.

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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https://thunlp.github.io/1/fewrel1.html
§ https://github.com/snipsco/nlu-
benchmark/
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https://github.com/clinc/oos-eval
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§ https://deepai.org/publication/meta-learning-for-few-shot-natural-language-processing-a-survey
§ https://paperswithcode.com/paper/siamese-neural-networks-for-one-shot-image
§ https://arxiv.org/abs/1606.04080
§ https://arxiv.org/abs/1703.05175
§ https://arxiv.org/abs/1711.06025