Multimodal NLP and Artificial Intelligence: Cross-Media Information Understanding and Generation
Yicheng Liu · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2024
In the era of rapid development of artificial intelligence, multimodal natural language processing (NLP) has emerged as a crucial field.This paper explores the significance and applications of multimodal NLP in cross-media information understanding and generation.By integrating multiple modalities such as text, images, audio, and video, multimodal NLP aims to enhance the accuracy and comprehensiveness of language understanding and generation.The paper discusses various techniques and models used in multimodal NLP, including deep learning architectures and attention mechanisms.It also examines the challenges and future directions of this field, highlighting the potential for improved human-computer interaction and intelligent applications.Through case studies and experimental results, the paper demonstrates the effectiveness of multimodal NLP in tasks such as image captioning, video description generation, and crossmodal retrieval.Overall, multimodal NLP holds great promise for advancing the capabilities of artificial intelligence and enabling more natural and seamless interaction between humans and machines.