Abstract
Label smoothing is a widely used technique in various domains, such as image classification and speech recognition, known for effectively combating model overfitting. However, there is few research on its application to text sentiment classification. To fill in the gap, this study investigates the implementation of label smoothing for sentiment classification by utilizing different levels of smoothing. The primary objective is to enhance sentiment classification accuracy by transforming discrete labels into smoothed label distributions. Through extensive experiments, we demonstrate the superior performance of label smoothing in text sentiment classification tasks across eight diverse datasets and deep learning architectures: TextCNN, BERT, and RoBERTa, under two learning schemes: training from scratch and fine-tuning.
Abstract (translated)
标签平滑是一种在各种领域中广泛使用的技术,如图像分类和语音识别,被广泛认为是有效对抗模型过拟合的有力工具。然而,标签平滑在文本情感分类中的应用研究较少。为了填补这一空白,本研究探讨了利用不同平滑级别的标签平滑来实施标签平滑在文本情感分类方面的应用。主要目标是通过将离散的标签转换为平滑的标签分布来提高情感分类准确性。通过广泛的实验,我们证明了标签平滑在八个不同数据集和深度学习架构上的文本情感分类任务中的卓越性能:TextCNN、BERT和RoBERTa,采用两种学习方案:从头开始训练和微调。
URL
https://arxiv.org/abs/2312.06522
