  In recent years, the generation of conversation content based on deep neural
networks has attracted many researchers. However, traditional neural language
models tend to generate general replies, lacking logical and emotional factors.
This paper proposes a conversation content generation model that combines
reinforcement learning with emotional editing constraints to generate more
meaningful and customizable emotional replies. The model divides the replies
into three clauses based on pre-generated keywords and uses the emotional
editor to further optimize the final reply. The model combines multi-task
learning with multiple indicator rewards to comprehensively optimize the
quality of replies. Experiments shows that our model can not only improve the
fluency of the replies, but also significantly enhance the logical relevance
and emotional relevance of the replies.
