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.

翻译：标签平滑是一种广泛应用于图像分类和语音识别等领域的经典技术，已被证明能有效缓解模型过拟合问题。然而，关于其在文本情感分类中的应用研究尚属空白。为填补这一研究缺口，本文通过采用不同平滑层级探究了标签平滑在情感分类中的实现方法。其主要目标是通过将离散标签转化为平滑标签分布来提升情感分类准确率。基于八个不同数据集和TextCNN、BERT、RoBERTa三种深度学习架构，分别在从头训练和微调两种学习范式下开展大量实验，验证了标签平滑在文本情感分类任务中的卓越性能。