---
title: "MedDialog: Large-scale Medical Dialogue Datasets (2020)"
description: "Building medical dialogue systems that could act as \"virtual doctors\" in telemedicine requires many… 24 references, cited by 74 papers here."
authors: ["Guangtao Zeng", "Wenmian Yang", "Zeqian Ju", "Yue Yang", "Sicheng Wang", "Ruisi Zhang", "Meng Zhou", "Jiaqi Zeng", "Xiangyu Dong", "Ruoyu Zhang", "Hongchao Fang", "Penghui Zhu", "Shu Chen", "Pengtao Xie"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.743/"
doi: "10.18653/v1/2020.emnlp-main.743"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zeng-2020-meddialog-large-scale-medical-dialogue/"
---
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# MedDialog: Large-scale Medical Dialogue Datasets

**Authors:** [Guangtao Zeng](https://md.research.elamur.ai/free/en/authors/guangtao-zeng/), Wenmian Yang, [Zeqian Ju](https://md.research.elamur.ai/free/en/authors/zeqian-ju/), [Yue Yang](https://md.research.elamur.ai/free/en/authors/yue-yang/), Sicheng Wang, Ruisi Zhang, Meng Zhou, [Jiaqi Zeng](https://md.research.elamur.ai/free/en/authors/jiaqi-zeng/), Xiangyu Dong, [Ruoyu Zhang](https://md.research.elamur.ai/free/en/authors/ruoyu-zhang/), Hongchao Fang, Penghui Zhu, Shu Chen and Pengtao Xie · **Year:** 2020 · **Venue:** [Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.743/) · [PDF](https://aclanthology.org/2020.emnlp-main.743.pdf) · [DOI 10.18653/v1/2020.emnlp-main.743](https://doi.org/10.18653/v1/2020.emnlp-main.743) · [OpenAlex](https://openalex.org/W3101223450)

**Citations:** 173 citations (OpenAlex, 2026-10-14) · [74](https://md.research.elamur.ai/free/en/papers/zeng-2020-meddialog-large-scale-medical-dialogue/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Building medical dialogue systems that could act as "virtual doctors" in telemedicine requires many real patient–doctor conversations. Privacy constraints make such data scarce, and earlier corpora were small or focused on a few diseases. The authors address this gap with MedDialog, a pair of public corpora gathered from online consultation platforms. MedDialog-CN, built from haodf.com, holds about 3.4 million Chinese consultations. MedDialog-EN, built from iCliniq and HealthCareMagic, holds roughly 0.26 million English consultations. Each record opens with the patient's description of their condition and history, followed by a multi-turn exchange with a doctor. As benchmarks, they trained Transformer, GPT and BERT-GPT response generators on MedDialog-CN. They then tested transfer learning by fine-tuning these models on the small CovidDialog-Chinese corpus. Pretraining on MedDialog-CN improved automatic metrics, most clearly for Transformer, and raised human ratings of relevance, informativeness and human-likeness for every model. The work shows that a large general medical dialogue corpus can support low-resource clinical dialogue tasks.

## Key points
- MedDialog-CN contains 3,407,494 dialogues, 11.3M utterances and 660.2M tokens. It spans 29 broad and 172 fine-grained specialties, with consultations dated 2010–2020 and an average of 3.3 utterances per dialogue.
- MedDialog-EN contains 0.26M dialogues, 0.51M utterances and 44.53M tokens. It covers 51 community categories and 96 specialties, with consultations dated 2008–2020.
- The transfer target was CovidDialog-Chinese, with 1,088 dialogues and 9,494 utterances. MedDialog-CN pretraining cut Transformer perplexity from 53.3 to 13.7 and raised its BLEU-2 from 5.7% to 7.8%.
- Human evaluation used 100 examples, 5 raters and a 1–5 scale. BERT-GPT with pretraining scored highest (human-likeness 3.44 vs. 3.78 for ground truth). Two-sided t-tests gave p-values mostly below 0.015.
- Data and code are released at github.com/UCSD-AI4H/Medical-Dialogue-System. The authors plan to annotate medical entities in future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of California San Diego](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) — University, San Diego, United States · 34 works · 51,167 citations

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🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works

## Cite

Cite as: Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen and Pengtao Xie. 2020. MedDialog: Large-scale Medical Dialogue Datasets. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zeng2020meddialog,
  title = {{MedDialog: Large-scale Medical Dialogue Datasets}},
  author = {Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen and Pengtao Xie},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.743},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.743}
}
```

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*Updated 24 October 2026*