  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.
