This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness of these techniques in augmenting training sets to improve performance in tasks such as topic classification, sentiment analysis, and offensive language detection. The research emphasizes not only the augmentation methods, but also the strategic order in which real and augmented instances are introduced during training. A major contribution is the development and evaluation of Modified Cyclical Curriculum Learning (MCCL) for augmented datasets, which represents a novel approach in the field. Results show that specific augmentation methods, especially when integrated with MCCL, significantly outperform traditional training approaches in NLP model performance. These results underscore the need for careful selection of augmentation techniques and sequencing strategies to optimize the balance between speed and quality improvement in various NLP tasks. The study concludes that the use of augmentation methods, especially in conjunction with MCCL, leads to improved results in various classification tasks, providing a foundation for future advances in text augmentation strategies in NLP.

这项研究全面评估了多种数据集和自然语言处理（NLP）任务中的文本增强技术，以解决这些方法缺乏可靠的广义证据的问题。研究重点关注训练集增强方法及现实实例与增强实例引入的策略顺序对训练的影响，尤其是通过开发和评估Modified Cyclical Curriculum Learning (MCCL)进行增强数据集。结果表明特定的增强方法，尤其是与MCCL结合使用，显著优于传统的训练方法在NLP模型性能方面。这些结果强调了在各种NLP任务中在速度和质量改进之间优化的需要，以及增强方法的谨慎选择和顺序策略的重要性。该研究得出结论，增强方法的使用，特别是与MCCL结合使用，可以改善各种分类任务的结果，为NLP中的文本增强策略的未来进展奠定了基础。

推进自然语言处理模型增强：增强方法和课程策略的综合研究