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.

翻译：人工智能驱动的聊天机器人已成为解决心理困扰的新兴方案。由于缺乏心理治疗数据，研究者利用从在线同伴支持论坛抓取的对话进行训练。但此类平台中的回复并非由专业人士提供，因此同时包含符合与不符合规范的回应。本研究尝试采用基于成熟行为编码方案——动机性访谈治疗完整性（MITI）代码改编的标签，识别在线心理困扰支持对话中的符合与不符合回应类型，并展示如何将某些回应类型改述为更符合动机性访谈（MI）原则的形式，从而使聊天机器人回复更契合MI策略。作为概念验证，我们通过微调Blender和GPT3构建了多个改述器，将不符合MI的"未经许可提供建议"回应改述为"经许可提供建议"。我们展示了如何通过构建伪平行语料库实现这一目标，从而避免人力成本。通过自动评估与人工评估，我们发现：当训练数据较少时，采用提示学习（prompting）与数据增强等技术，能够生成保留原始文本内容且符合目标风格的优质改述结果。