mlsanityData to 7 Oct 2026

DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation

arXiv 2408.06044 ↗First posted 12 Aug 2024cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

22 of 48 references.

References · 22

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  11. 2019AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognitioncited by 87
  12. 2018EmotionLines: An Emotion Corpus of Multi-Party Conversationscited by 80
10 more
  1. 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 67
  2. 2019Target-Guided Open-Domain Conversationcited by 66
  3. 2022PAL: Persona-Augmented Emotional Support Conversation Generationcited by 39
  4. 2018Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressionscited by 21
  5. 2021Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interactioncited by 21
  6. 2020EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systemscited by 19
  7. 2024SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Trackingcited by 17
  8. 2021Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generationcited by 15
  9. 2024StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generationcited by 5
  10. 2024KoCoSa: Korean Context-aware Sarcasm Detection Datasetcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memorycited by 13
  2. 2025DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilitiescited by 2
  3. 2026EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agentscited by 2
  4. 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agentscited by 1
  5. 2026Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistancecited by 0
  6. 2026AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurementcited by 0