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Generating Responses Expressing Emotion in an Open-Domain Dialogue System

Part of the Lecture Notes in Computer Science book series (LNISA,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.


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Correspondence to Osmar R. Zaïane .

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Huang, C., Zaïane, O.R. (2019). Generating Responses Expressing Emotion in an Open-Domain Dialogue System. In: Bodrunova, S., et al. Internet Science. INSCI 2018. Lecture Notes in Computer Science(), vol 11551. Springer, Cham.

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  • Print ISBN: 978-3-030-17704-1

  • Online ISBN: 978-3-030-17705-8

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