Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. We focus on continual text classification under the class-incremental setting. Recent CL studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. In this paper, through an in-depth exploration of the representation learning process in CL, we discover that the compression effect of the information bottleneck leads to confusion on analogous classes. To enable the model learn more sufficient representations, we propose a novel replay-based continual text classification method, InfoCL. Our approach utilizes fast-slow and current-past contrastive learning to perform mutual information maximization and better recover the previously learned representations. In addition, InfoCL incorporates an adversarial memory augmentation strategy to alleviate the overfitting problem of replay. Experimental results demonstrate that InfoCL effectively mitigates forgetting and achieves state-of-the-art performance on three text classification tasks. The code is publicly available at https://github.com/Yifan-Song793/InfoCL.

翻译：持续学习旨在随时间不断学习新知识，同时避免对旧任务的灾难性遗忘。本文聚焦于类增量设置下的持续文本分类任务。近期持续学习研究发现，相似类别上的性能严重下降是导致灾难性遗忘的关键因素。通过深入探究持续学习中的表示学习过程，我们发现信息瓶颈的压缩效应会导致相似类别的混淆。为使模型学习更充分的表示，我们提出了一种新颖的基于回放的持续文本分类方法InfoCL。该方法利用快慢对比学习与当前-过去对比学习实现互信息最大化，以更有效地恢复先前学到的表示。此外，InfoCL引入对抗记忆增强策略缓解回放导致的过拟合问题。实验结果表明，InfoCL能有效缓解遗忘，并在三个文本分类任务上达到最先进性能。代码已开源至https://github.com/Yifan-Song793/InfoCL。