# Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification

Source: [Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification](<https://www.sota2.com/research/paper/68fa72441e209131cea5561f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:22:12.513Z

Authors: Yijie Gao, Shijing Si, Hua Luo, Haixia Sun, Yugui Zhang

Published: 2023-12-11T17:00:35.000Z

## Abstract

Label smoothing is a widely used technique in various domains, such as text classification, image classification and speech recognition, known for effectively combating model overfitting. However, there is little fine-grained analysis on how label smoothing enhances text sentiment classification. To fill in the gap, this article performs a set of in-depth analyses on eight datasets for text sentiment classification and three deep learning architectures: TextCNN, BERT, and RoBERTa, under two learning schemes: training from scratch and fine-tuning. By tuning the smoothing parameters, we can achieve improved performance on almost all datasets for each model architecture. We further investigate the benefits of label smoothing, finding that label smoothing can accelerate the convergence of deep models and make samples of different labels easily distinguishable.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.06522>)

[PDF](<https://arxiv.org/pdf/2312.06522.pdf>)