# Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification
###### Abstract  
Deep learning techniques have demonstrated significant advancements in the task of text classification. Regrettably, the majority of these techniques necessitate a substantial corpus of annotated data to achieve optimal performance. Meta-learning has yielded intriguing outcomes in few-shot learning tasks, showcasing its potential in advancing the field. However, the current meta-learning methodologies are susceptible to overfitting due to the mismatch between a small number of samples and the complexity of the model. To mitigate this concern, we propose a Prompt-based Graph Convolutional Adversarial (PGCA) meta-learning framework, aiming to improve the adaptability of complex models in a few-shot scenario. Firstly, leveraging prompt learning, we generate embedding representations that bridge the downstream tasks. Then, we design a meta-knowledge extractor based on a graph convolutional neural network (GCN) to capture inter-class dependencies through instance-level interactions. We also integrate the adversarial network architecture into a meta-learning framework to extend sample diversity through adversarial training and improve the ability of the model to adapt to new tasks. Specifically, we mitigate the impact of extreme samples by introducing external knowledge to construct a list of class prototype extensions. Finally, we conduct a series of experiments on four public datasets to demonstrate the effectiveness of our proposed method. meta-learning; prompt learning; graph convolutional neural network; adversarial network +
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the pre-training model to retrieve pertinent information from the pre-training phase that is specifically relevant to the target task. In general, the downstream task has to introduce additional parameters to adapt to the corresponding task needs. However, the downstream task model has difficulty learning the knowledge needed for the task quickly with a few samples. Meta-learning is a standard means of addressing this issue. Through several meta-training tasks, meta-learning tries to enhance a model's capacity for learning, enabling the model to swiftly adapt to new tasks with a little amount of training data. Numerous meta-learning-based techniques [12; 13; 14; 15; 16] have proven to be superior in tackling challenges involving few-shot text categorization. However, the existing meta-learning methods suffer from the problem of overfitting caused by the mismatch of a few samples on the complex model. Ref. [17] first proposed the introduction of adversarial domain adaptive networks to enhance the generalization capability of the meta-learning framework. Meanwhile, the majority of the few-shot text categorization techniques now in use [15] primarily emphasize information transmission between samples belonging to the same category, neglecting the relationships between different categories. Graph neural networks (GNNs) can effectively provide richer semantic representations by capturing the relationships between nodes through inter-instance information transfer and aggregation over graph structures, which is well suited for small-sample learning. In this paper, we propose a Prompt-based Graph Convolutional Adversarial (PGCA) meta-learning network framework. First, we leverage prompt learning and pre-trained language models to obtain text embeddings adapted to downstream tasks. Then, we construct a GCN-based meta-knowledge extractor, allowing feature interactions among a few samples to capture the relationship information between nodes and fully extract the internal meta-knowledge of the text, generating higher-quality text embedding representations for the text classification task. In addition, we incorporate the adversarial network into the meta-learning framework, where the generator and discriminator are represented by GCN and feed-forward neural networks, respectively. We extend the text embedding feature space through adversarial training and enhance sample diversity.