Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification

作     者：Li, Mengyu Liu, Yonghao Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu 

作者机构：Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education College of Computer Science and Technology Jilin University China University of Trento Italy 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Classification (of information) 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.