# Medical Dialogue Generation via Dual Flow Modeling

arXiv: https://arxiv.org/abs/2305.18109 · HTML: https://mlsanity.com/paper/2305.18109/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation](https://mlsanity.com/paper/2012.11988/) (2020)
- [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021)
- [Diaformer: Automatic Diagnosis via Symptoms Sequence Generation](https://mlsanity.com/paper/2112.10433/) (2021)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- ["My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics](https://mlsanity.com/paper/2204.13953/) (2022)
- [FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows](https://mlsanity.com/paper/2202.06633/) (2022)

## Built on by (most cited first, 10 of 10)

- [MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding](https://mlsanity.com/paper/2403.06611/) (2024)
- [TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments](https://mlsanity.com/paper/2504.21851/) (2025)
- [Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis](https://mlsanity.com/paper/2401.06541/) (2024)
- [ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue](https://mlsanity.com/paper/2409.17610/) (2024)
- [Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment](https://mlsanity.com/paper/2506.10877/) (2025)
- [From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department](https://mlsanity.com/paper/2608.23627/) (2026)
- [Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failure](https://mlsanity.com/paper/2505.03675/) (2025)
- [GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2505.12888/) (2025)
- [Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations](https://mlsanity.com/paper/2511.00514/) (2025)
- [Understanding Clinical Cognitive Dialogues Using Large Language Models](https://mlsanity.com/paper/2609.34125/) (2026)

## Abstract (arXiv)

Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn, as they help the understanding of how the dialogue flows and enhance the prediction of the entities and dialogue acts to be adopted in the following turn. Correspondingly, we propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework. It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively. We employ two sequential models to encode them and devise an interweaving component to enhance their interactions. Experiments on two datasets demonstrate that our method exceeds baselines in both automatic and manual evaluations.