  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.
