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

arXiv: https://arxiv.org/abs/2408.06044 · HTML: https://mlsanity.com/paper/2408.06044/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [PAL: Persona-Augmented Emotional Support Conversation Generation](https://mlsanity.com/paper/2212.09235/) (2022)
- [Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions](https://mlsanity.com/paper/1811.08592/) (2018)
- [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021)
- [EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems](https://mlsanity.com/paper/2003.02958/) (2020)
- [SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking](https://mlsanity.com/paper/2402.02285/) (2024)
- [Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation](https://mlsanity.com/paper/2105.11696/) (2021)
- [StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation](https://mlsanity.com/paper/2403.11439/) (2024)
- [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://mlsanity.com/paper/2402.14428/) (2024)

## Built on by (most cited first, 6 of 6)

- [Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory](https://mlsanity.com/paper/2409.15084/) (2024)
- [DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities](https://mlsanity.com/paper/2507.05750/) (2025)
- [EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents](https://mlsanity.com/paper/2604.07549/) (2026)
- [A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents](https://mlsanity.com/paper/2601.10809/) (2026)
- [Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance](https://mlsanity.com/paper/2606.18613/) (2026)
- [AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement](https://mlsanity.com/paper/2608.12329/) (2026)

## 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.