# Meta-learning for Few-shot Natural Language Processing: A Survey

arXiv: https://arxiv.org/abs/2007.09604 · HTML: https://mlsanity.com/paper/2007.09604/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 30 of 42)

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- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
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- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners](https://mlsanity.com/paper/2205.09229/) (2022)
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- [MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling](https://mlsanity.com/paper/2206.02040/) (2022)
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- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [TIDE: Test Time Few Shot Object Detection](https://mlsanity.com/paper/2311.18358/) (2023)

## Abstract (arXiv)

Few-shot natural language processing (NLP) refers to NLP tasks that are accompanied with merely a handful of labeled examples. This is a real-world challenge that an AI system must learn to handle. Usually we rely on collecting more auxiliary information or developing a more efficient learning algorithm. However, the general gradient-based optimization in high capacity models, if training from scratch, requires many parameter-updating steps over a large number of labeled examples to perform well (Snell et al., 2017). If the target task itself cannot provide more information, how about collecting more tasks equipped with rich annotations to help the model learning? The goal of meta-learning is to train a model on a variety of tasks with rich annotations, such that it can solve a new task using only a few labeled samples. The key idea is to train the model's initial parameters such that the model has maximal performance on a new task after the parameters have been updated through zero or a couple of gradient steps. There are already some surveys for meta-learning, such as (Vilalta and Drissi, 2002; Vanschoren, 2018; Hospedales et al., 2020). Nevertheless, this paper focuses on NLP domain, especially few-shot applications. We try to provide clearer definitions, progress summary and some common datasets of applying meta-learning to few-shot NLP.