Can Triplet Loss Be Used for Multi-Label Few-Shot Classification? A Case Study

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gergely Márk Csányi, Renátó Vági, Andrea Megyeri, Anna Fülöp , Dániel Nagy, János Pál Vadász, István Üveges
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引用次数: 0

Abstract

Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle multi-label classification. We conducted a case study to identify any limitations in its application. The experiments were conducted on a dataset containing Hungarian legal decisions of administrative agencies in tax matters belonging to a major legal content provider. We also tested how different Siamese embeddings compare on classifying a previously non-existing label on a binary and a multi-label setting. We found that triplet-trained Siamese networks can be applied to perform classification but with a sampling restriction during training. We also found that the overlap between labels affects the results negatively. The few-shot model, seeing only ten examples for each label, provided competitive results compared to models trained on tens of thousands of court decisions using tf-idf vectorization and logistic regression.
三重态损失可以用于多标签少针分类吗?案例研究
少次学习是深度学习的一个分支,也是目前研究的热点。本文解决了最初为多类分类设计的三重训练的Siamese网络能否有效处理多标签分类的研究问题。我们进行了一个案例研究,以确定其应用中的任何限制。实验是在一个数据集上进行的,该数据集包含匈牙利行政机构在税务问题上的法律决定,该决定属于一个主要的法律内容提供商。我们还测试了不同的暹罗嵌入如何在二进制和多标签设置上对以前不存在的标签进行分类。我们发现三重训练的Siamese网络可以应用于分类,但在训练过程中有采样限制。我们还发现,标签之间的重叠会对结果产生负面影响。与使用tf-idf矢量化和逻辑回归训练的数以万计的法院判决模型相比,每个标签只看到10个样本的few-shot模型提供了具有竞争力的结果。
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
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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