Utterance understanding and emotion generation in a dialogue system for children

Abstract

We propose a dialogue system for children with emotions. In this paper, we focus on utterance understanding and emotion generation in this dialogue system. In utterance understanding, we extract utterance intention and focus. Machine learning based on utterances collected from college students gives an accuracy of about 70% for utterance intention analysis and about 90% for focus extraction for children's utterances. In emotion generation, we deal with two types of emotion: short emotion and mood. The type of response is affected by mood, and short emotion is expressed as a facial expression. Regarding expressions of emotion, college students judged about 60% of emotions generated in dialogue as valid, but we could not confirm their usefulness in dialogue with children.