Enhancing Neural Machine Translation for Low-Resource Languages through Innovative Techniques and Approaches

Machine translation is a critical area in natural language processing, aiming to achieve automatic translation between different languages. Despite significant progress in multilingual translation tasks with neural machine translation models, many challenges remain in handling low-resource languages. These challenges mainly arise from the limited availability of parallel corpora for training and the differences in grammatical structures and expressions among languages. Based on this, this study proposes an iterative ensemble pruning data augmentation strategy, combined with a multimodal model that incorporates glyph and pinyin features. First, the iterative ensemble pruning method is used to enhance data quality and quantity. Second, a multimodal model utilizing glyph and pinyin features is constructed to achieve more accurate translation of low-resource languages. Finally, these two subtasks are collaboratively optimized to improve model performance. Experimental validation on the Uighur-Chinese medical bilingual dataset provided by Xinjiang University and the CCEval multilingual machine translation evaluation set demonstrates that the proposed method outperforms existing baseline models in terms of translation accuracy and robustness. The experimental results indicate that the iterative ensemble pruning strategy effectively improves data quality, and the multimodal model significantly enhances the translation performance of low-resource languages. In summary, the proposed method not only performs excellently in low-resource language machine translation but also provides new ideas and methods for data augmentation and multimodal modeling in other natural language processing tasks, having significant research significance and application value.