  Recent advancements in conversational systems have significantly enhanced
human-machine interactions across various domains. However, training these
systems is challenging due to the scarcity of specialized dialogue data.
Traditionally, conversational datasets were created through crowdsourcing, but
this method has proven costly, limited in scale, and labor-intensive. As a
solution, the development of synthetic dialogue data has emerged, utilizing
techniques to augment existing datasets or convert textual resources into
conversational formats, providing a more efficient and scalable approach to
dataset creation. In this survey, we offer a systematic and comprehensive
review of multi-turn conversational data generation, focusing on three types of
dialogue systems: open domain, task-oriented, and information-seeking. We
categorize the existing research based on key components like seed data
creation, utterance generation, and quality filtering methods, and introduce a
general framework that outlines the main principles of conversation data
generation systems. Additionally, we examine the evaluation metrics and methods
for assessing synthetic conversational data, address current challenges in the
field, and explore potential directions for future research. Our goal is to
accelerate progress for researchers and practitioners by presenting an overview
of state-of-the-art methods and highlighting opportunities to further research
in this area.
