Contextual Aware AI Translation Models in Cross Language Information Retrieval

With the acceleration of globalization, the demand for cross-language information retrieval is increasing. The traditional machine translation technology has some limitations, and it is difficult to accurately capture the semantic differences between the source and target languages. In this study, an artificial intelligence translation model based on context-awareness is proposed. The model utilizes deep learning technology to improve the accuracy and fluency of translation by establishing the semantic correspondence between the source and target languages while considering the contextual information of the text. The study adopts a large-scale parallel corpus for model training and evaluation, and conducts comparative experiments with traditional translation models. The experimental results show that compared with the traditional translation model, the context-aware AI translation model proposed in this study has significant improvements in all performance indexes. The model not only performs well in the laboratory environment, but also shows great promise in practical applications. In the future, we will further optimize the model architecture and extend it to more languages to provide more powerful support for cross-lingual information acquisition.