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.

翻译：开发面向心理健康支持的专业对话系统需要多轮对话数据，这一问题近年来受到日益广泛的关注。然而，由于数据隐私保护以及所需时间和成本等因素，收集并发布大规模真实场景的多轮对话以推动心理健康领域的进展仍面临诸多挑战。为应对数据稀缺问题，我们提出SMILE——一种从单轮到多轮的包容性语言扩展技术，通过提示ChatGPT将公开的单轮对话改写为多轮对话。本研究首先进行语言转换分析，验证了所提方法相较于其他基线方法的可行性；随后开展对话多样性研究，涵盖词汇特征、语义特征及对话主题，证明了所提方法的有效性。此外，通过专家评估发现，采用本方法生成的对话质量显著高于其他基线方法。基于此，我们利用所提方法生成了大规模、多样且高质量的对话数据集SmileChat，包含总计55,165段对话，平均每段对话包含10.4轮交互。最后，利用该语料库开发了心理健康聊天机器人MeChat。为全面评估SmileChat的质量，我们收集了包含82段真实咨询对话的数据集用于模型评估。自动评估与人工评估均表明，训练后的对话系统性能得到显著提升，验证了SmileChat的高质量与实用性。