Research Area:  Machine Learning
Emotional conversation generation has elicited a wide interest in both academia and industry. However, existing emotional neural conversation systems tend to ignore the necessity to combine topic and emotion in generating responses, possibly leading to a decline in the quality of responses. This paper proposes a topic-enhanced emotional conversation generation model that incorporates emotional factors and topic information into the conversation system, by using two mechanisms. First, we use a Twitter latent Dirichlet allocation (LDA) model to obtain topic words of the input sequences as extra prior information, ensuring the consistency of content between posts and responses for emotional conversation generation. Second, the system uses a dynamic emotional attention mechanism to adaptively acquire content-related and affective information of the input texts and extra topics. The advantage of this study lies in the fact that the presented model can generate abundant emotional responses, with the contents being related and diverse. To demonstrate the effectiveness of our method, we conduct extensive experiments on large-scale Weibo post–response pairs. Experimental results show that our method achieves good performance, even outperforming some existing models.
Emotional Conversation Generation
Author(s) Name:  Yehong Peng, Yizhen Fang, Zhiwen Xie, Guangyou Zhou
Journal name:  Knowledge-Based Systems
Publisher name:  ELSEVIER
Volume Information:  Volume 163, 1 January 2019, Pages 429-437
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S095070511830457X