SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support

TL;DR

This paper introduces SMILE, a novel method leveraging ChatGPT to convert single-turn dialogues into multi-turn conversations for mental health support, creating a large, diverse dataset and a high-quality chatbot while addressing data privacy concerns.

Contribution

The paper presents a new language expansion technique using ChatGPT to generate multi-turn dialogues from single-turn data, facilitating mental health dialogue system development.

Findings

SMILE effectively transforms single-turn dialogues into diverse multi-turn conversations.

SMILECHAT dataset contains 55,000 high-quality dialogues for mental health support.

The resulting mental health chatbot, MeChat, shows improved performance in evaluations.

Abstract

Developing specialized dialogue systems for mental health support requires multi-turn conversation data, which has recently garnered increasing attention. However, gathering and releasing large-scale, real-life multi-turn conversations that could facilitate advancements in mental health support presents challenges in data privacy protection and the time and cost involved in crowdsourcing. To address these challenges, we introduce SMILE, a single-turn to multi-turn inclusive language expansion technique that prompts ChatGPT to rewrite public single-turn dialogues into multi-turn ones. Our work begins by analyzing language transformation and validating the feasibility of our proposed method. We conduct a study on dialogue diversity, including lexical features, semantic features, and dialogue topics, demonstrating the effectiveness of our method. Further, we employ our method to generate a large-scale, lifelike, and diverse dialogue dataset named SMILECHAT, consisting of 55k dialogues. Finally, we utilize the collected corpus to develop a mental health chatbot, MeChat. To better assess the quality of SMILECHAT, we collect a small-scale real-life counseling dataset conducted by data anonymization. Both automatic and human evaluations demonstrate significant improvements in our dialogue system and confirm that SMILECHAT is high-quality. Code, data, and model are publicly available at https://github.com/qiuhuachuan/smile.