Enhancing Integrity Modeling for Emotional Conversation Generation

Establishing an open-domain emotional conversation generation with content and emotional dependence is a key step toward intelligent interactions. However, the researchers only dedicated to adding the thematic content or emotional words and handling both factors are not yet properly solved. In this article, we aim to address the issue of integrity in emotional content which indicates the response has a dependency relationship between theme and emotion. We propose a thematic–emotional conversation model, which consists of parallel channels and joint encoding, to maximize integrity in conversation generation. We first use the encoding of the parallel channel to strengthen the decoding weight of the theme and emotion. The joint encoding of the dual attention mechanism and the thematic emotional tradeoff strategy are involved in the encoding to ensure that the theme complements the emotional content during the response generation. After that, the bidirectional training interactions between post and response are calculated to derive the new response inference vectors. Experiment results suggest that the proposed model is effective not only in content integrity but also in emotion.