A Survey on Recent Advances in Conversational Data Generation

arXiv:2405.13003v1 Announce Type: new
Abstract: 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 …

abstract advances arxiv conversational conversational data crowdsourcing cs.ai cs.cl cs.ir data data generation datasets dialogue domains however human interactions labor machine scale survey systems through training type