  Developing specialized dialogue systems for mental health support requires
multi-turn conversation data, which has recently garnered increasing attention.
However, gathering and releasing large-scale and real-life multi-turn
conversations to facilitate advancements in mental health presents challenges
due to data privacy protection, as well as the time and cost involved. To
address the challenges related to data scarcity, we introduce SMILE, a
single-turn to multi-turn inclusive language expansion technique that prompts
ChatGPT to rewrite public single-turn dialogues into multi-turn ones. Our work
begins with the analysis of language transformation, validating the feasibility
of the proposed method when compared with other baseline methods. We then
conduct a study on dialogue diversity, including lexical features, semantic
features, and dialogue topics, demonstrating the effectiveness of our proposed
method. Furthermore, we implement an expert evaluation and the results
demonstrate that the dialogues generated with our proposed method are of higher
quality than those generated with other baseline methods. Thus, we employ our
method to generate a large-scale, diverse, and high-quality dialogue dataset
named SmileChat, comprising 55,165 dialogues in total with an average of 10.4
turns per dialogue. Finally, we utilize the collected corpus to develop a
mental health chatbot, MeChat. To better assess the overall quality of
SmileChat, we collect a real-life chat dataset comprising 82 counseling
dialogues for model evaluation. Both automatic and human evaluations
demonstrate that our trained dialogue system exhibits significant improvements,
showcasing that SmileChat is high-quality and practical.
