Topic-enhanced emotional conversation generation with attention mechanism

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 pap...

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Bibliographic Details
Published in:Knowledge-based systems 2019-01, Vol.163, p.429-437
Main Authors: Peng, Yehong, Fang, Yizhen, Xie, Zhiwen, Zhou, Guangyou
Format: Article
Language:English
Subjects:
Attention mechanism
Dirichlet problem
Emotional conversation
Emotional factors
Emotions
Information
Knowledge
Sequence-to-sequence
Topic model
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Summary: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. •We present a topic-enhanced neural emotion conversation generation model (TE-ECG) with attention mechanism.•The topic words are obtained from a pre-trained Twitter LDA model to ensure the generated response is related to the post.•A novel dynamic emotional attention mechanism is proposed to capture the emotional context and topic information.•The TE-ECG model can generate responses at both the emotion- and content-related levels.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.09.006