MKA：一种面向医疗对话任务生成模型的可扩展医学知识辅助机制
MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative Models on Medical Conversation Tasks
AI总结：
本文提出可扩展医学知识辅助机制MKA，通过包含6类医学信息的医学知识图谱和特定token拼接策略，将医学知识注入通用神经生成模型，在MedDG和MedDialog-CN上取得更优性能，其中MKA-Bert-GPT达到最先进水平。
摘要：
利用自然语言处理（NLP）技术开发医疗聊天机器人，可以使患者诊断更加便捷和高效，这是医疗人工智能中的典型应用。由于其重要性，已有大量相关研究涌现。近年来，神经生成模型作为聊天机器人的核心展现出了令人印象深刻的能力，但由于缺乏医学特定知识，直接应用于医疗对话时难以良好扩展。为了解决这一局限性，本文提出了一种可扩展的医学知识辅助机制 MKA。该机制旨在辅助通用神经生成模型在医疗对话任务上取得更好性能。在该机制中设计了医学特定知识图谱，其中包含6类医学相关信息，包括科室、药物、检查、症状、疾病、食物。此外，定义了特定的token拼接策略，以有效地将医学信息注入输入数据。我们在两个典型医疗数据集 MedDG 和 MedDialog-CN 上对所提方法进行了评估。评估结果表明，结合该机制的模型在多个自动评估指标上优于原始方法。此外，MKA-Bert-GPT 达到了最先进的性能。开源代码已公开：https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism
英文摘要：
Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of research have been come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable Medical Knowledge Assisted mechanism, MKA, is proposed in this paper. The mechanism aims to assist general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes are public: https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
