# An Empathetic AI Coach for Self-Attachment Therapy

arXiv: https://arxiv.org/abs/2209.08316 · HTML: https://mlsanity.com/paper/2209.08316/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Ethical Challenges in Data-Driven Dialogue Systems](https://mlsanity.com/paper/1711.09050/) (2017)
- [Towards Understanding Emotional Intelligence for Behavior Change Chatbots](https://mlsanity.com/paper/1907.10664/) (2019)
- [#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment](https://mlsanity.com/paper/1909.02809/) (2019)

## Built on by (most cited first, 5 of 5)

- [Emotional Support with LLM-based Empathetic Dialogue Generation](https://mlsanity.com/paper/2507.12820/) (2025)
- [Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales](https://mlsanity.com/paper/2310.00010/) (2023)
- [From Words and Exercises to Wellness: Farsi Chatbot for Self-Attachment Technique](https://mlsanity.com/paper/2310.09362/) (2023)
- [A Multilingual Virtual Guide for Self-Attachment Technique](https://mlsanity.com/paper/2310.18366/) (2023)
- [Structure Matters: Evaluating Multi-Agents Orchestration in Generative Therapeutic Chatbots](https://mlsanity.com/paper/2603.00774/) (2026)

## Abstract (arXiv)

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user's text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received.