# CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

arXiv: https://arxiv.org/abs/2212.06049 · HTML: https://mlsanity.com/paper/2212.06049/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## 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)
- [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)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [Global-to-local Memory Pointer Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1901.04713/) (2019)
- [ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation](https://mlsanity.com/paper/1907.05339/) (2019)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [Multi-level Memory for Task Oriented Dialogs](https://mlsanity.com/paper/1810.10647/) (2018)
- [On the Generation of Medical Dialogues for COVID-19](https://mlsanity.com/paper/2005.05442/) (2020)
- [Learning to Infer Entities, Properties and their Relations from Clinical Conversations](https://mlsanity.com/paper/1908.11536/) (2019)
- [Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?](https://mlsanity.com/paper/2011.09708/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Conversational Diagnostic AI](https://mlsanity.com/paper/2401.05654/) (2024)
- [Follow-up Question Generation For Enhanced Patient-Provider Conversations](https://mlsanity.com/paper/2503.17509/) (2025)
- [MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures](https://mlsanity.com/paper/2410.15463/) (2024)
- [Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment](https://mlsanity.com/paper/2506.10877/) (2025)
- [Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences](https://mlsanity.com/paper/2404.08666/) (2024)

## Abstract (arXiv)

The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic. The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus. We make the very first attempt to release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites. We annotate each utterance of the conversation with seven different categories of medical entities, including diseases, symptoms, medical tests, medical history, remedies, medications and other aspects as additional labels. Finally, we propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems. We use pre-trained language models for dialogue generation, incorporating annotated medical entities, to generate a virtual doctor's response that addresses the patient's query. Experimental results show that the proposed dialog models perform comparably better when supplemented with entity information and hence can improve the response quality.