Label Smoothing for Enhanced Text Sentiment Classification

Label smoothing (LS) proceeds by using soft targets that are a weighted average of the hard Label smoothing is a widely used technique targets and the uniform distribution over labels in various domains, such as image (Müller et al., 2019; Lienen and Hüllermeier, classification and speech recognition, 2021). This technique lessens the disparity between known for effectively combating model the top probability estimate and the remaining overfitting. However, there is few research ones, thereby acting as a barrier to the model on its application to text sentiment from generating extremely confident predictions, classification. To fill in the gap, this consequently decreasing the model's likelihood of study investigates the implementation of becoming excessively tailored to the training data label smoothing for sentiment classification (Lukasik et al., 2020; Gao et al., 2020).