AI-driven chatbots have become an emerging solution to address psychological distress. Due to the lack of psychotherapeutic data, researchers use dialogues scraped from online peer support forums to train them. But since the responses in such platforms are not given by professionals, they contain both conforming and non-conforming responses. In this work, we attempt to recognize these conforming and non-conforming response types present in online distress-support dialogues using labels adapted from a well-established behavioral coding scheme named Motivational Interviewing Treatment Integrity (MITI) code and show how some response types could be rephrased into a more MI adherent form that can, in turn, enable chatbot responses to be more compliant with the MI strategy. As a proof of concept, we build several rephrasers by fine-tuning Blender and GPT3 to rephrase MI non-adherent "Advise without permission" responses into "Advise with permission". We show how this can be achieved with the construction of pseudo-parallel corpora avoiding costs for human labor. Through automatic and human evaluation we show that in the presence of less training data, techniques such as prompting and data augmentation can be used to produce substantially good rephrasings that reflect the intended style and preserve the content of the original text.

本研究利用在线支持论坛的对话数据，使用名为MITI的行为编码模式，尝试识别在在线心理疾患支持对话中存在的符合和不符合类型，并构建重述器使用数据增强和提示技术来提高Chatbot的MI一致性。经过自动和人工评估，本研究证明了该方法在数据量较少时也能产生反映预期风格且保留原始文本内容的好的重述结果。

采用动机访谈策略提升心理困扰支持对话响应