A Multimodal Model for College English Teaching Using Text and Image Feature Extraction

Abstract

The rapid development of the internet and multimedia technology in recent years has continued to push foreign language education in the direction of modern education. Multimodal education is becoming more and more important in the field of English education as an advanced educational concept in the field of language education. As a result, many English teachers have begun to emphasize the use of multimodal teaching theory in their classrooms. This paper investigates a multimodal model that incorporates text and image features, based on multimodal discourse theory, systemic functional linguistics theory, and foreign language teaching theory. This paper develops a multimodal model that can search for images and texts from various perspectives. We use an image feature bias term in the log-bilinear natural language model to influence the probability of predicting the next word based on the context, resulting in a multimodal model. The experimental results show that the proposed model, as an image-text relationship evaluation index system, has a slower search speed than other models but better search accuracy.