A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning

TL;DR

This paper introduces a hybrid approach combining transfer learning and meta-learning to improve few-shot text classification, demonstrating significant performance gains over traditional methods in low-data scenarios.

Contribution

It presents a novel hybrid model that leverages transfer and meta-learning for enhanced few-shot text classification, validated through comprehensive experiments.

Findings

Outperforms traditional methods in few-shot settings

Transfer and meta-learning components are crucial for accuracy

Model adapts rapidly to new tasks with limited data

Abstract

With the continuous development of natural language processing (NLP) technology, text classification tasks have been widely used in multiple application fields. However, obtaining labeled data is often expensive and difficult, especially in few-shot learning scenarios. To solve this problem, this paper proposes a few-shot text classification model based on transfer learning and meta-learning. The model uses the knowledge of the pre-trained model for transfer and optimizes the model's rapid adaptability in few-sample tasks through a meta-learning mechanism. Through a series of comparative experiments and ablation experiments, we verified the effectiveness of the proposed method. The experimental results show that under the conditions of few samples and medium samples, the model based on transfer learning and meta-learning significantly outperforms traditional machine learning and deep learning methods. In addition, ablation experiments further analyzed the contribution of each component to the model performance and confirmed the key role of transfer learning and meta-learning in improving model accuracy. Finally, this paper discusses future research directions and looks forward to the potential of this method in practical applications.