A Survey on Recent Advances in Conversational Data Generation

Understand

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.