Siamese meta-learning network for social disputes based on multi-head attention.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-06-04 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2910
Jing Wang, Rui Zhang, Huijian Han, Yuxiang Liu, Zhaoxing Peng
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引用次数: 0

Abstract

Few-shot learning has been widely used in scenarios where labeled data is scarce, where meta-learning based few-shot classification is widely used, such as the Siamese network. Although the Siamese network has achieved good results in some applications, there are still some problems: (1) When computing prototype vectors with external knowledge of class labels, it depends on the quality and correctness of class labels. (2) When processing data, the Siamese network is not sufficient to capture dependencies between long distance. (3) When the data is complex or the samples are unbalanced, the Siamese network does not achieve the best performance. Therefore, this article proposes a multi-head attention siamese meta-learning network (MASM). Specifically, this article uses synonym substitution to solve the problem that the computation of prototype vectors will be transitionally dependent on class label. In addition, we use the multi-head attention mechanism to capture long-distance dependence by exploiting its global perception capability, which further improves the model performance. We conducted experiments on four benchmark datasets, all of which achieved good performance, and also applied the model for the first time in the field of social disputes, and experimented on a homemade private dispute dataset, which also achieved good results.

基于多头注意的社会纠纷Siamese元学习网络。
在标记数据稀缺的场景中,基于元学习的Few-shot分类被广泛使用,例如Siamese网络。虽然Siamese网络在一些应用中取得了很好的效果,但仍然存在一些问题:(1)在具有类标签外部知识的情况下计算原型向量时,依赖于类标签的质量和正确性。(2)在处理数据时,暹罗网络不足以捕捉长距离之间的依赖关系。(3)当数据复杂或样本不平衡时,Siamese网络不能达到最佳性能。因此,本文提出了一个多头关注连体元学习网络(MASM)。具体来说,本文使用同义词替换来解决原型向量计算过渡依赖于类标号的问题。此外,我们利用多头注意机制的全局感知能力来捕获远距离依赖,进一步提高了模型的性能。我们在4个基准数据集上进行了实验,均取得了较好的表现,同时也首次将该模型应用于社会纠纷领域,并在自制的私人纠纷数据集上进行了实验,也取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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