Topic-enhanced emotional conversation generation with attention mechanism
| Autor: | Yizhen Fang, Guangyou Zhou, Yehong Peng, Zhiwen Xie |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Computer science Mechanism (biology) media_common.quotation_subject 02 engineering and technology Affect (psychology) Latent Dirichlet allocation Management Information Systems symbols.namesake Consistency (negotiation) Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Conversation Quality (business) Software Cognitive psychology media_common |
| Zdroj: | Knowledge-Based Systems. 163:429-437 |
| ISSN: | 0950-7051 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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