A WGAN-Based Dialogue System for Embedding Humor, Empathy, and Cultural Aspects in Education

Abstract

Artificial intelligence (AI) technologies have been utilized in the education industry for enhancing student's performance by generating spontaneous, timely, and personalized query response. One such technology is a dialogue system which is capable of generating humorous and empathetic responses for enhancing students' learning outcomes. There is, however, limited research on the combination of humor, empathy, and culture in education. Thus, this paper proposes a dialogue system that is based on Wasserstein's Generative Adversarial Network (WGAN) for generating responses with humor, empathy, and cultural sensitivity. The dialogue system has the ability to generate responses that take into account both coarse-grained emotions at the conversation level and fine-grained emotions at the token level, allowing for a nuanced understanding of a student's emotional state. It can utilize external knowledge and prior context to enhance the ability of AI dialogue systems to comprehend emotions in a multimodal context. It can also analyze large corpora of text and other data, providing valuable insights into cultural context, semantic properties, and language variations. The dialogue system is a promising AI technology that can improve learning outcomes in various academic fields by generating responses with humor, empathy, and cultural sensitivity. In our study, the dialogue system achieved an accuracy rate of 94.12%, 93.83% and 92.60% in humor, empathy and culture models, respectively.