ConvCounsel: A Conversational Dataset for Student Counseling
This addresses the student mental health crisis by providing a dataset for building counseling dialogue systems, though it is incremental as it focuses on a specific strategy within an existing domain.
The paper tackles the problem of limited student counseling resources by introducing ConvCounsel, a specialized dataset for mental health dialogue systems, and demonstrates its utility with the NYCUKA system, showing improved results in developing reliable pipelines.
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.