Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2021
Abstract
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the prior disease-symptom relations. Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues. More importantly, by dynamically evolving disease-symptom graphs, GEML also well addresses the real-world challenges that the disease-symptom correlations of each disease may vary or evolve along with more diagnostic cases. Extensive experiment results on the CMDD dataset and our newly-collected Chunyu dataset testify the superiority of our approach over state-of-the-art approaches. Besides, our GEML can generate an enriched dialogue-sensitive knowledge graph in an online manner, which could benefit other tasks grounded on knowledge graph.
Discipline
Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, February 2-9
First Page
13362
Last Page
13370
Identifier
10.1609/aaai.v35i15.17577
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
LIN, Shuai; ZHOU, Pan; LIANG, Xiaodan; TANG, Jianheng; ZHAO, Ruihui; CHEN, Ziliang; and LIN, Liang.
Graph-evolving meta-learning for low-resource medical dialogue generation. (2021). Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), Virtual Conference, February 2-9. 13362-13370.
Available at: https://ink.library.smu.edu.sg/sis_research/9027
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v35i15.17577