Summary of Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models, by Xinyuan Wang et al.
Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models
by Xinyuan Wang, Haozhou Li, Dingfang Zheng, Qinke Peng
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses challenges in traditional healthcare systems by leveraging online medical services for triage and consultation. It highlights two key issues: limited publicly available, domain-specific medical datasets due to privacy concerns, and methods that lack medical knowledge and struggle with professional terminology. To overcome these obstacles, the authors create a large-scale Chinese Medical Dialogue Corpora (LCMDC) and propose a novel triage system combining BERT-based supervised learning and prompt learning, as well as a GPT-based consultation model. The pre-trained PLMs are further enhanced by using a self-constructed background corpus. Experimental results on the LCMDC demonstrate the effectiveness of these proposed systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making healthcare services better online. There are two big problems: there aren’t many big medical datasets that the public can use, and current methods don’t understand medical language well. The authors create a huge database for Chinese medical conversations (LCMDC) and design new systems to help with triage and consultation. They train their models using a special background corpus. Results show these new systems work well. |
Keywords
» Artificial intelligence » Bert » Gpt » Prompt » Supervised
