Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
This work addresses the need for more meaningful and emotionally customizable conversations in AI systems, though it appears incremental as it builds on existing neural language models with added constraints.
The paper tackled the problem of generating general and emotionless replies in neural conversation models by proposing a reinforcement learning approach with emotional editing constraints, resulting in improved fluency, logical relevance, and emotional relevance of the generated replies.
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.