# Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy

arXiv: https://arxiv.org/abs/2305.10195 · HTML: https://mlsanity.com/paper/2305.10195/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [ParlAI: A Dialog Research Software Platform](https://mlsanity.com/paper/1705.06476/) (2017)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Politeness Transfer: A Tag and Generate Approach](https://mlsanity.com/paper/2004.14257/) (2020)
- [Evaluating Style Transfer for Text](https://mlsanity.com/paper/1904.02295/) (2019)
- [IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation](https://mlsanity.com/paper/1901.11333/) (2019)
- [Semi-supervised Text Style Transfer: Cross Projection in Latent Space](https://mlsanity.com/paper/1909.11493/) (2019)
- [Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2010.00735/) (2020)
- [Formality Style Transfer with Hybrid Textual Annotations](https://mlsanity.com/paper/1903.06353/) (2019)
- [Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms](https://mlsanity.com/paper/2004.04999/) (2020)
- [Non-Parallel Text Style Transfer with Self-Parallel Supervision](https://mlsanity.com/paper/2204.08123/) (2022)
- [Structured Content Preservation for Unsupervised Text Style Transfer](https://mlsanity.com/paper/1810.06526/) (2018)
- [Exploring Self-Identified Counseling Expertise in Online Support Forums](https://mlsanity.com/paper/2106.12976/) (2021)

## Built on by (most cited first, 15 of 15)

- [ESCoT: Towards Interpretable Emotional Support Dialogue Systems](https://mlsanity.com/paper/2406.10960/) (2024)
- [CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering](https://mlsanity.com/paper/2403.16008/) (2024)
- [Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors](https://mlsanity.com/paper/2403.15482/) (2024)
- [A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions](https://mlsanity.com/paper/2502.11095/) (2025)
- [Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning](https://mlsanity.com/paper/2403.15737/) (2024)
- [Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation](https://mlsanity.com/paper/2403.13578/) (2024)
- [Is ChatGPT More Empathetic than Humans?](https://mlsanity.com/paper/2403.05572/) (2024)
- [Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy](https://mlsanity.com/paper/2411.06723/) (2024)
- [Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation](https://mlsanity.com/paper/2506.05073/) (2025)
- [KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy](https://mlsanity.com/paper/2502.05651/) (2025)
- [A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit](https://mlsanity.com/paper/2505.17362/) (2025)
- [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025)
- [Mixed Chain-of-Psychotherapies for Emotional Support Chatbot](https://mlsanity.com/paper/2409.19533/) (2024)
- [Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations](https://mlsanity.com/paper/2509.10184/) (2025)
- [PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling](https://mlsanity.com/paper/2601.19096/) (2026)

## Abstract (arXiv)

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.