Research on the design of French reading comprehension assistance system supported by multimodal artificial intelligence

This paper designs a French reading comprehension assistance system based on multimodal artificial intelligence, focusing on the problems such as difficult vocabulary, grammar and understanding of cultural background existing in French learning. The system integrates three modalities: text, image and speech. It adopts the Transformer structure and attention mechanism to achieve deep semantic parsing and has functions such as vocabulary recognition, grammar annotation and cultural supplementation. The experimental results show that this system outperforms the single-modal system in terms of vocabulary accuracy, grammar parsing ability and cultural information recognition, and has significant performance improvement and practical value.