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.
Abstract (translated)
随着自然语言处理（NLP）技术的不断进步，文本分类任务已经在多个应用领域得到了广泛的应用。然而，在获取标注数据时往往成本高昂且难度较大，特别是在小样本学习场景中尤为明显。为解决这一问题，本文提出了一种基于迁移学习和元学习的少量样本文本分类模型。该模型利用预训练模型的知识进行知识转移，并通过元学习机制优化模型在小样本任务中的快速适应能力。通过一系列对比实验和消融实验，我们验证了所提方法的有效性。实验结果显示，在小样本和中等样本条件下，基于迁移学习和元学习的模型明显优于传统的机器学习和深度学习方法。此外，消融实验证明了每个组件对模型性能的贡献，并确认了迁移学习与元学习在提高模型准确性中的关键作用。最后，本文探讨了未来的研究方向，并展望了该方法在实际应用中的潜力。
URL
https://arxiv.org/abs/2502.09086
