  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.
