In recent years,researchers have been dedicated to enhancing the emotional intelligence of dialogue systems,yet have overlooked the feedback elements within dialogues.These models tend to produce uninteresting and simplistic general re-sponses.To address this issue,we propose an emotional conversation reinforcement learning(EC-RL)approach.This method gen-erates response statements with specified emotions based on the context of the conversation,and evaluates the future reward of the dialogue from both content quality and emotional aspects to improve the coherence of the generated statements.Utilizing the NLPCC 2017 emotional dialogue dataset,experiments demonstrate that,compared to existing methods,our proposed model enables emotionally controllable dialogue response generation,producing fluent and diverse text content.
维普
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