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.

使用基于常识知识图的低资源医学对话生成，通过少量数据适应性地将源疾病的诊断经验转移到目标疾病，并通过 Graph-Evolving Meta-Learning 框架学习演变常识图来推理新疾病的疾病症状相关性。这种方法的实验结果表明其在 CMDD 数据集和新收集的 Chunyu 数据集上表现卓越。