Sentiment-guided Commonsense-aware Response Generation for Mental Health Counseling

TL;DR

This paper introduces EmpRes, a novel sentiment-guided, commonsense-aware response generation model for virtual mental health assistants, improving response quality and user satisfaction in mental health counseling applications.

Contribution

The paper presents EmpRes, a new framework that integrates sentiment guidance and commonsense knowledge into response generation for mental health support, demonstrating superior performance and user acceptance.

Findings

EmpRes outperforms existing baselines on the HOPE dataset.

Human evaluation shows EmpRes's responses are comparable or better than gold standards.

Over 85% of users are willing to continue using and recommend EmpRes.

Abstract

The crisis of mental health issues is escalating. Effective counseling serves as a critical lifeline for individuals suffering from conditions like PTSD, stress, etc. Therapists forge a crucial therapeutic bond with clients, steering them towards positivity. Unfortunately, the massive shortage of professionals, high costs, and mental health stigma pose significant barriers to consulting therapists. As a substitute, Virtual Mental Health Assistants (VMHAs) have emerged in the digital healthcare space. However, most existing VMHAs lack the commonsense to understand the nuanced sentiments of clients to generate effective responses. To this end, we propose EmpRes, a novel sentiment-guided mechanism incorporating commonsense awareness for generating responses. By leveraging foundation models and harnessing commonsense knowledge, EmpRes aims to generate responses that effectively shape the client's sentiment towards positivity. We evaluate the performance of EmpRes on HOPE, a benchmark counseling dataset, and observe a remarkable performance improvement compared to the existing baselines across a suite of qualitative and quantitative metrics. Moreover, our extensive empirical analysis and human evaluation show that the generation ability of EmpRes is well-suited and, in some cases, surpasses the gold standard. Further, we deploy EmpRes as a chat interface for users seeking mental health support. We address the deployed system's effectiveness through an exhaustive user study with a significant positive response. Our findings show that 91% of users find the system effective, 80% express satisfaction, and over 85.45% convey a willingness to continue using the interface and recommend it to others, demonstrating the practical applicability of EmpRes in addressing the pressing challenges of mental health support, emphasizing user feedback, and ethical considerations in a real-world context.