An excellent dialogue system needs to not only generate rich and diverse logical responses but also meet the needs of users for emotional communication. However, despite much work, these two problems have not been solved. In this paper, we propose a model based on conditional variational autoencoder and dual emotion framework (CVAE-DE) to generate emotional responses. In our model, latent variables of the conditional variational autoencoder are adopted to promote the diversity of conversation. A dual emotion framework is adopted to control the explicit emotion of the response and prevent the conversation from generating emotion drift indicating that the emotion of the response is not related to the input sentence. A multiclass emotion classifier based on the Bidirectional Encoder Representations from Transformers (BERT) model is employed to obtain emotion labels, which promotes the accuracy of emotion recognition and emotion expression. A large number of experiments show that our model not only generates rich and diverse responses but also is emotionally coherent and controllable.

中文翻译：

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基于条件变分自动编码器和双重情感框架的情感对话生成

一个优秀的对话系统不仅需要产生丰富多样的逻辑响应，还需要满足用户进行情感交流的需求。但是，尽管做了很多工作，但这两个问题仍未解决。在本文中，我们提出了一个基于条件变分自动编码器和双重情感框架（CVAE-DE）的模型来生成情感反应。在我们的模型中，采用条件变分自动编码器的潜在变量来促进对话的多样性。采用双重情感框架来控制响应的显式情感，并防止对话产生情绪漂移，从而表明响应的情感与输入句子无关。利用基于变压器的双向编码器表示的多类情感分类器获取情感标签，从而提高了情感识别和情感表达的准确性。大量实验表明，我们的模型不仅会产生丰富多样的响应，而且在情感上是连贯且可控的。