A Survey on Recent Advances in Conversational Data Generation

TL;DR

This survey reviews recent methods for generating synthetic conversational data, addressing challenges in dataset creation for dialogue systems and categorizing approaches across different system types.

Contribution

It provides a comprehensive framework and categorization of existing research on multi-turn conversational data generation, including evaluation and future directions.

Findings

Synthetic data generation improves scalability and cost-efficiency.

Existing methods are categorized into seed data, utterance generation, and quality filtering.

The survey highlights current challenges and potential research opportunities.

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 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.