ConvCounsel: A Conversational Dataset for Student Counseling

TL;DR

This paper introduces ConvCounsel, a specialized mental health dialogue dataset focusing on active listening, to aid the development of conversational counseling systems amid high student-to-counselor ratios.

Contribution

It provides a new speech and text dataset for mental health counseling, addressing ethical constraints of existing datasets, and demonstrates its utility with a dialogue system.

Findings

ConvCounsel improves mental health dialogue system development.

NYCUKA system benefits from the ConvCounsel dataset.

The dataset facilitates active listening strategy in counseling AI.

Abstract

Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting periods for in-person consultations, which cause suboptimal treatment. Significant efforts have been directed toward developing mental health dialogue systems utilizing the existing open-source mental health-related datasets. However, currently available datasets either discuss general topics or various strategies that may not be viable for direct application due to numerous ethical constraints inherent in this research domain. To address this issue, this paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. This dataset comprises both speech and text data, which can facilitate the development of a reliable pipeline for mental health dialogue systems. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset. The results show the merit of using this dataset.