  Traditional Chinese Medicine (TCM) is an influential form of medical
treatment in China and surrounding areas. In this paper, we propose a TCM
prescription generation task that aims to automatically generate a herbal
medicine prescription based on textual symptom descriptions.
Sequence-to-sequence (seq2seq) model has been successful in dealing with
sequence generation tasks. We explore a potential end-to-end solution to the
TCM prescription generation task using seq2seq models. However, experiments
show that directly applying seq2seq model leads to unfruitful results due to
the repetition problem. To solve the problem, we propose a novel decoder with
coverage mechanism and a novel soft loss function. The experimental results
demonstrate the effectiveness of the proposed approach. Judged by professors
who excel in TCM, the generated prescriptions are rated 7.3 out of 10. It shows
that the model can indeed help with the prescribing procedure in real life.
