# On the Generation of Medical Dialogues for COVID-19

arXiv: https://arxiv.org/abs/2005.05442 · HTML: https://mlsanity.com/paper/2005.05442/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 12 of 12)

- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021)
- [LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs](https://mlsanity.com/paper/2204.09220/) (2022)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- [Songs Across Borders: Singable and Controllable Neural Lyric Translation](https://mlsanity.com/paper/2305.16816/) (2023)
- [CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation](https://mlsanity.com/paper/2212.06049/) (2022)
- [Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges](https://mlsanity.com/paper/2405.10630/) (2024)
- [MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge](https://mlsanity.com/paper/2406.02919/) (2024)
- [ProMRVL-CAD: Proactive Dialogue System with Multi-Round Vision-Language Interactions for Computer-Aided Diagnosis](https://mlsanity.com/paper/2502.10620/) (2025)

## Abstract (arXiv)

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets -- CovidDialog -- (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog tasks. We perform both automatic and human evaluation of responses generated by these models. The results show that the generated responses are promising in being doctor-like, relevant to the conversation history, and clinically informative. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue.