融合迁移与元学习，提升小样本文本分类效果
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning
- 利用预训练模型知识进行迁移，增强模型初始能力
- 通过元学习机制提升模型在少量样本下的快速适应能力
- 适合数据稀缺场景，对NLP小样本任务有实用价值
随着自然语言处理技术的不断发展，文本分类已广泛应用于多个领域。然而，在少样本学习场景中获取标注数据往往成本高昂且困难。为此，本文提出一种基于迁移学习与元学习的小样本文本分类模型。该模型利用预训练模型的知识实现迁移，并通过元学习机制优化模型在少量样本任务中的快速适应能力。一系列对比实验与消融实验验证了所提方法的有效性。实验结果表明，在少样本和中等样本条件下，该模型显著优于传统机器学习与深度学习方法。消融实验进一步分析了各组件对模型性能的贡献，证实了迁移学习与元学习在提升准确率中的关键作用。最后，本文探讨了未来研究方向，展望了该方法在实际应用中的潜力。
原文摘要 · Abstract (English)
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.
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