In an open-domain dialogue system, recognition and expression of emotions are the key factors for success. Most of the existing research related to Chinese dialogue systems aims at improving the quality of content but ignores the expression of human emotions. In this article, we propose a Chinese emotional dialogue response generation algorithm based on reinforcement learning that can generate responses not only according to content but also according to emotion. In the proposed method, a multi-emotion classification model is first used to add emotion labels to the corpus of post-response pairs. Then, with the help of reinforcement learning, the reward function is constructed based on two aspects, namely, emotion and content. Among the generated candidates, the system selects the one with long-term success as the best reply. At the same time, to avoid safe responses and diversify dialogue, a diversity beam search algorithm is applied in the decoding process. The comparative experiments demonstrate that the proposed model achieves satisfactory results according to both automatic and human evaluations.

中文翻译：

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通过强化学习生成中文情感对话反应

在开放域对话系统中，情感的识别和表达是成功的关键因素。现有与中文对话系统相关的研究大多以提高内容质量为目标，而忽略了人类情感的表达。在本文中，我们提出了一种基于强化学习的中文情感对话响应生成算法，该算法不仅可以根据内容生成响应，还可以根据情感生成响应。在所提出的方法中，首先使用多情感分类模型将情感标签添加到响应后对的语料库中。然后，借助强化学习，基于情感和内容两个方面构建奖励函数。在生成的候选人中，系统选择长期成功的候选人作为最佳回复。同时，为了避免安全响应和多样化对话，在解码过程中应用了分集束搜索算法。比较实验表明，所提出的模型根据自动和人工评估均取得了令人满意的结果。