通过任务自适应度量空间提升少样本文本分类的原型准确性
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
- 利用标签信息构建自适应度量空间，缩小类内差异、扩大类间差异
- 结合最优传输技术，用查询集样本更准确估计类别原型
- 无需外部资源，在8个数据集上全面超越现有方法
元学习在少样本文本分类中表现优异，但现有方法常因类内差异大、类间差异小，难以从支持集准确提取类别原型。现有方案虽引入外部知识或预训练模型增强数据，但需额外资源，不适用于多数少样本场景。本文提出新方法，充分挖掘任务内部信息：利用标签构建任务自适应度量空间，动态缩小类内差异、放大类间差异；进一步采用最优传输技术，结合查询集样本共同估计类别原型，缓解因类内差异过大导致的支持集样本不准确问题。在8个基准数据集上广泛实验表明，该方法在所有任务和数据集上均显著优于当前最优模型。代码与数据已公开于https://github.com/YvoGao/LAQDA。
原文摘要 · Abstract (English)
Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences and small inter-class differences within the task. Recent approaches attempt to incorporate external knowledge or pre-trained language models to augment data, but this requires additional resources and thus does not suit many few-shot scenarios. In this paper, we propose a novel solution to address this issue by adequately leveraging the information within the task itself. Specifically, we utilize label information to construct a task-adaptive metric space, thereby adaptively reducing the intra-class differences and magnifying the inter-class differences. We further employ the optimal transport technique to estimate class prototypes with query set samples together, mitigating the problem of inaccurate and ambiguous support set samples caused by large intra-class differences. We conduct extensive experiments on eight benchmark datasets, and our approach shows obvious advantages over state-of-the-art models across all the tasks on all the datasets. For reproducibility, all the datasets and codes are available at https://github.com/YvoGao/LAQDA.
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