Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach

TL;DR

This paper introduces PARTNER, a reinforcement learning-based system that enhances empathy in online mental health conversations by transforming low-empathy posts into more supportive and understanding responses, thereby improving the quality of digital mental health support.

Contribution

The paper presents a novel reinforcement learning approach for empathic rewriting in online mental health support, combining transformer-based language models with reward mechanisms for empathy and quality.

Findings

PARTNER generates more empathic responses than baseline methods.

The system maintains conversation fluency and context specificity.

Human evaluations favor PARTNER's outputs for empathy and diversity.

Abstract

Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. However, recent studies have shown that highly empathic conversations are rare in online mental health platforms. In this paper, we work towards improving empathy in online mental health support conversations. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy. Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context. Here we propose PARTNER, a deep reinforcement learning agent that learns to make sentence-level edits to posts in order to increase the expressed level of empathy while maintaining conversation quality. Our RL agent leverages a policy network, based on a transformer language model adapted from GPT-2, which performs the dual task of generating candidate empathic sentences and adding those sentences at appropriate positions. During training, we reward transformations that increase empathy in posts while maintaining text fluency, context specificity and diversity. Through a combination of automatic and human evaluation, we demonstrate that PARTNER successfully generates more empathic, specific, and diverse responses and outperforms NLP methods from related tasks like style transfer and empathic dialogue generation. Our work has direct implications for facilitating empathic conversations on web-based platforms.