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2019 | OriginalPaper | Buchkapitel

Generating Responses Expressing Emotion in an Open-Domain Dialogue System

verfasst von : Chenyang Huang, Osmar R. Zaïane

Erschienen in: Internet Science

Verlag: Springer International Publishing

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Abstract

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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Vorheriges Kapitel Adapting a Conversational Text Generator for Online Chatbot Messaging

Nächstes Kapitel Measuring User Experience in Chatbots: An Approach to Interpersonal Communication Competence

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Titel: Generating Responses Expressing Emotion in an Open-Domain Dialogue System
verfasst von: Chenyang Huang
Osmar R. Zaïane
Verlag: Springer International Publishing
Buch: Internet Science
Print ISBN: 978-3-030-17704-1

Electronic ISBN: 978-3-030-17705-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17705-8_9

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