Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy

Anuradha Welivita, Pearl Pu


Abstract
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.
Anthology ID:
2023.findings-acl.334
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5411–5432
Language:
URL:
https://aclanthology.org/2023.findings-acl.334
DOI:
10.18653/v1/2023.findings-acl.334
Bibkey:
Cite (ACL):
Anuradha Welivita and Pearl Pu. 2023. Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5411–5432, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy (Welivita & Pu, Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/2023.findings-acl.334.pdf
Video:
 https://preview.aclanthology.org/improve-issue-templates/2023.findings-acl.334.mp4