Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.

翻译：对话系统的进步已彻底革新了信息获取方式，突破了单次查询的局限。然而，开发对话系统需要大量训练数据，这在低资源领域和语言中是一项挑战。传统数据收集方法（如众包）劳动密集且耗时，在此情境下效果甚微。数据增强（DA）是缓解对话系统中数据稀缺问题的有效途径。本教程全面且最新地概述了对话系统中数据增强的方法，重点介绍了对话增强、开放域及任务导向型对话生成的最新进展，以及评估这些模型的不同范式。我们还讨论了当前挑战与未来方向，以帮助研究人员和实践者进一步推动该领域的发展。