Optimization of data analysis models for low‐resource Eurasian languages using machine translation

Abstract This study explores low‐resource language data translation models in the realms of multimedia teaching and cyber security. A rapid learning‐based neural machine translation (NMT) method is developed based on meta‐learning theory. Subsequently, the back translation method is employed to further improve the NMT model for low‐resource language data. Results indicate that the proposed low‐resource language NMT method based on meta‐learning achieves increased Bilingual Evaluation Understudy (BLEU) scores for three target tasks in a supervised environment. This study emphasizes the auxiliary role of meta‐learning theory in low‐resource language data translation, aiming to enhance the efficiency of translation models in utilizing information from low‐resource languages.