# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 2 Background Literature
### Meta-Learning for Few-Shot Text Classification  
Deep learning models that require large-scale labeled data have significant limitations in their training and application process. First, in terms of model training, it takes a lot of time and money to build a large-scale labeled dataset, and it is also practically challenging to build a new training dataset suitable for a given domain for each specific task. Based on this awareness of the problem, diverse learning methodologies such as transfer learning were proposed, and a methodology to learn the neural network connection weight in advance and use it to train the neural network for a new task was introduced to train the model more efficiently [19; 20; 21; 22]. In contrast, humans can quickly learn new tasks by using what they have learned in the past. For example, humans can learn the rules of multiplication based on their knowledge of addition or solve problems such as finding the person in a picture of multiple people based on a single facial picture. Bridging this gap in learning methods between AI and humans is very important.  
Meta-learning is a useful methodology that can effectively perform model learning and generalize in an area using only these low resources. In the meta-learning process, \"learning to learn\" does not mean learning a model suitable for a specific task but a method of learning to effectively perform various tasks, as shown in research [23]. To this end, meta-learning performs learning on various tasks through the meta-train phase, enabling rapid generalization to low-resource data.  
Three common meta-learning approaches are metric-based meta-learning, model-based meta-learning, and optimization-based meta-learning. Metric-based meta-learning uses a notion of distance in a certain space to compare tasks and transfer information between similar tasks. The core idea is to learn a function that captures the similarity (or difference) between different data points and use this function to make predictions for new tasks. In this method, instead of learning a mapping from input to output, the model learns an appropriate metric space where classification can be performed by computing distances to examples from the training set [24; 25; 26; 27; 28].  
In model-based meta-learning, the objective is to learn a model of the learning process itself. This model can then be used to predict the outcomes of different learning strategies for new tasks. The acquired knowledge from previous tasks is used to build the new model, which in turn is used to make predictions for new tasks. One popular method in this category is Model-Agnostic Meta-Learning (MAML) [23], which aims to find a model initialization that can be fine-tuned quickly for new tasks. The last approach, optimization-based meta-learning, as the name suggests, is focused on optimizing the learning process. The idea is to adjust the learning algorithm to better suit the task at hand. The learning process is framed as an optimization problem, and techniques from the field of optimization are used to find the best solution [29]. Our proposed model, Uncertainty-Aware Active Meta-Learning (UA-AML), also implements a similar methodology where a model trains on different tasks, and the knowledge obtained from the training process is used for future tasks. It is unique because each task is selected by measuring uncertainty, allowing a high-quality selection.