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Graph-EvolvingMeta-Learning forLow-Resource MedicalDialogue Generation Shuai LinPan ZhouXiaodan Liang2021 год

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
статья (материалы конференций)

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: 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.
Год издания: 2021
Авторы: Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin
Издательство: Association for the Advancement of Artificial Intelligence
Источник: Proceedings of the AAAI Conference on Artificial Intelligence
Ключевые слова: Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications
Другие ссылки: Proceedings of the AAAI Conference on Artificial Intelligence (PDF)
Proceedings of the AAAI Conference on Artificial Intelligence (HTML)
Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (HTML)
Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)