Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification
This addresses imbalanced text classification, an incremental improvement over existing supervised contrastive learning methods.
The paper tackles the problem of data imbalance in supervised contrastive learning for text classification by proposing SharpReCL, which uses prototype vectors to rebalance the contrastive learning process, achieving results that outperform popular large language models on several datasets.
Text classification is a crucial and fundamental task in natural language processing. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification, some limitations remain. First, many text datasets are imbalanced, and the learning mechanism of supervised contrastive learning is sensitive to data imbalance, which may harm the model performance. Moreover, these models leverage separate classification branch with cross entropy and supervised contrastive learning branch without explicit mutual guidance. To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. First, we obtain the prototype vector of each class in the balanced classification branch to act as a representation of each class. Then, by further explicitly leveraging the prototype vectors, we construct a proper and sufficient target sample set with the same size for each class to perform the supervised contrastive learning procedure. The empirical results show the effectiveness of our model, which even outperforms popular large language models across several datasets.