Application and optimization of multimodal translation robots in Spanish medical literature

In recent years, neural network machine translation (NMT) technology has rapidly developed and received widespread attention from both academia and industry. In the medical field, high-quality MT is of great significance in promoting cross-border medical services, medical literature exchange, and other aspects. However, medical literature often has complex text structures, numerous professional terms, and frequent new words, which pose significant challenges to traditional MT methods. Multimodal neural machine translation (MNMT) can enhance the understanding and expression of source text content by introducing auxiliary information such as images. This article proposes an MNMT model based on fine-grained alignment of semantic guidance between text and images. This model first processes graphic and textual information through cross modal interaction, extracting fine-grained semantic features between the text and images. Subsequently, a gating mechanism was introduced to effectively align and fuse fine-grained information from multiple modalities into the text modality, thereby achieving deep fusion of multimodal features of text and text, and improving translation accuracy and semantic expression ability. The results indicate that the model effectively improves the accuracy and stability of the translation system, providing new technical support for cross linguistic communication in the medical field.