Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results.

翻译：中医处方是中医治疗最重要形式，揭示症状与中药之间复杂的非线性关系对于临床实践和辅助医生诊疗具有重要意义。尽管已有一些关于中医处方生成的研究，但这些研究仅考虑单一因素，主要基于症状描述直接建模症状-处方生成问题，缺乏中医知识的指导。为此，我们提出了一种基于RoBERTa和知识增强的中医处方生成模型（RoKEPG）。RoKEPG首先通过我们构建的中医语料库进行预训练，随后对预训练模型进行微调，并通过注意力掩码矩阵引入四类中医知识引导模型生成中医处方。在公开的中医处方数据集上的实验结果表明，RoKEPG在F1指标上相比最优基线模型提升了约2%。