Abstract
The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumption is often broken, i.e.~the distribution of the test data changes over time, which hinders the application of conventional ML models. One domain where the distribution shift naturally occurs is text classification, since people always find new topics to discuss. To this end, we survey research articles studying open-set text classification and related tasks. We divide the methods in this area based on the constraints that define the kind of distribution shift and the corresponding problem formulation, i.e.~learning with the Universum, zero-shot learning, and open-set learning. We next discuss the predominant mitigation approaches for each problem setup. Finally, we identify several future work directions, aiming to push the boundaries beyond the state of the art. Interestingly, we find that continual learning can solve many of the issues caused by the shifting class distribution. We maintain a list of relevant papers at this https URL.
Abstract (translated)
机器学习（ML）模型的基本假设是训练数据和测试数据来自相同的分布。然而，在实际应用中，这一假设经常被打破，即测试数据的分布会随时间发生变化，这限制了传统机器学习模型的应用范围。在文本分类领域，这种分布变化自然发生，因为人们总是会讨论新的话题。为此，我们总结了研究开放集文本分类及相关任务的研究文章。我们将该领域的方法根据定义分布变化类型的约束以及相应的问题表述进行分类，即基于全集的学习、零样本学习和开放集学习。接下来，我们将讨论每种问题设置下的主要缓解措施。最后，我们确定了一些未来的工作方向，旨在推动超越当前技术水平的边界。有趣的是，我们发现连续学习可以解决由类别分布变化引起许多问题。我们在[这里](https://this.url/)维护了一个相关论文列表。
URL
https://arxiv.org/abs/2502.12965
