Abstract
Emotional intelligence is one of the key parts of human intelligence. Exploring how to endow conversation models with emotional intelligence is a recent research hotspot. Although several emotional conversation approaches have been introduced, none of these methods were able to decide an appropriate emotion category for the response. We propose a new neural conversation model which is able to produce reasonable emotion interaction and generate emotional expressions. Experiments show that our proposed approaches can generate appropriate emotion and yield significant improvements over the baseline methods in emotional conversation.
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Acknowledgements
This work is supported by the Science and Technology Program of Guangzhou, China(No. 201802010025), the Fundamental Research Funds for the Central Universities(No. 2017BQ024), the Natural Science Foundation of Guangdong Province(No. 2017A030310428) and the University Innovation and Entrepreneurship Education Fund Project of Guangzhou(No. 2019PT103). The authors also thank the editors and reviewers for their constructive editing and reviewing, respectively.
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Zhang, R., Wang, Z. (2018). Learning to Converse Emotionally Like Humans: A Conditional Variational Approach. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_9
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DOI: https://doi.org/10.1007/978-3-319-99495-6_9
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