Zara returns: Improved personality induction and adaptation by an empathetic virtual agent

Abstract:

Virtual agents need to adapt their personality to the user in order to become more empathetic. To this end, we developed Zara the Supergirl, an interactive empathetic agent, using a modular approach. In this paper, we describe the enhanced personality module with improved recognition from speech and text using deep learning frameworks. From raw audio, an average F-score of 69.6 was obtained from real-time personality assessment using a Convolutional Neural Network (CNN) model. From text, we improved personality recognition results with a CNN model on top of pre-trained word embeddings and obtained an average F-score of 71.0. Results from our Human-Agent Interaction study confirmed our assumption that people have different agent personality preferences. We use insights from this study to adapt our agent to user personality.