D iag ESC : Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation (2024)

Abstract: Dialogue systems for mental health care aim to provide appropriate support to individuals experiencing mental distress. While extensive research has been conducted to deliver adequate emotional support, existing studies cannot identify individuals who require professional medical intervention and cannot offer suitable guidance. We introduce the Diagnostic Emotional Support Conversation task for an advanced mental health management system. We develop the DESC dataset to assess depression symptoms while maintaining user experience by utilizing task-specific utterance generation prompts and a strict filtering algorithm. Evaluations by professional psychological counselors indicate that DESC has a superior ability to diagnose depression than existing data. Additionally, conversational quality evaluation reveals that DESC maintains fluent, consistent, and coherent dialogues.

## introduction as interest in preventing and treating mental illnesses like depression, anxiety disorders, and panic disorders grows, dialogue system studies on mental health care are gaining attention. several studies have shown that chatbots can effectively manage the mental health of individuals, particularly in frontline settings, before seeking professional help @xcite . these chatbots provide emotional empathy and assist in finding stability for those facing emotional, mental, and psychological distress. mental health care also involves the early detection of illnesses. although delayed treatment aggravates symptoms and requires more complex treatment, it is challenging for individuals to self-diagnose @xcite . therefore, detecting diseases during conversation is an important factor, and we focus on depression, a representative mental illness. our research aims for an advanced conversation system to facilitate comprehensive mental health management. this system should provide extensive emotional support to individuals while simultaneously employing diagnostic questions to detect early signs of depression proactively. to achieve this goal, we define a novel task, diagnostic emotional support conversation (di-agesc), based on emotional support conversation (esc) @xcite . esc aims to support by helping reduce the seeker (user)'s mental stress. we also synthesize and release the dataset desc for this task. we synthesize utterances to ask questions about depression symptoms while maintaining a positive user experience. additionally, task-specific strict filtering algorithms ensure data quality. figure 1 shows part of the dialogue sample in desc. it includes questions about depression symptoms and labels regarding symptom frequency, enabling assessing the severity of depression. appropriate advice based on the severity of depression helps the individual receive help. professional psychological counselors validate the diagnostic ability and conversational quality of desc. ## problem formulation diagesc consists of three sub-tasks-two modes response generation, persona generation, and diagnosis generation. the dual modes of response generation encompass emotional support and diagnostic responses. persona generation is extracting characteristics based on the seeker's previous utterance, as suggested by pal @xcite . utilizing the previous persona as input can increase the user experience by facilitating reflection on an individual's characteristics and serving as a form of memory when the dialogue history cannot include all utterances. diagnosis generation, introduced for diagesc, involves generating symptom and corresponding score pairs. equation 1 refers to diagesc f at turn t that generates response r @xmath0, persona p t , and diagnosis pair d @xmath1, score} given the persona sentences p @xmath2, p 2 , . . . , p t } and dialogue history c @xmath3, s 1 , u 2 , s 2 , . . . , u t }. @xmath4, diagnostic} denotes the mode for response. u t and s t represent t th seeker (user) and supporter (system) utterance respectively. ## methodology we synthesize the dataset for diagesc named desc through a four-step process, as illustrated in figure 2 . initially, the source data undergoes pre-processing to align with the task requirements (section 4.1). phq-9-based dialogue generation is for generating conversations that ask and answer about symptoms of depression (section 4.2). each task-specific prompt is based on the patient health questionnaire-9 (phq-9) symptom item. the process includes filtering for reliability. the severity of depression is then calculated based on the answers obtained by the seeker. section 4.3 is to inform the seeker of appropriate advice. finally, to enhance reliability, expert verification is conducted on the validation and test datasets (section 4.4). ## conclusion this work proposes the diagesc task for a comprehensive mental health care dialogue system that goes beyond the limitations of supportive dialogue systems that do not detect mental risk. diagesc contributes to emotional support and early detection of depression, an important part of mental health. we have released the novel dataset desc by synthesizing diagnostic conversations based on a depression self-diagnosis questionnaire with emotional support data. task-specific prompts and strict filtering protocols facilitate questions about depression symptoms while ensuring continued user engagement. evaluation by a psychological counseling expert proves that desc has superior diagnostic performance and conversational quality. we hope that diagesc will contribute significantly to developing more effective and supportive dialogue systems in mental health care. moreover, the release of the desc dataset provides a valuable resource for the research community, encouraging further advancements and innovations in this critical area.