Generating Responses Expressing Emotion in an Open-Domain Dialogue System

  • Chenyang Huang
  • Osmar R. ZaïaneEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11551)


Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.


Open-domain dialogue generation Emotion Seq2seq Attention mechanism 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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