TLDALRLNMT: Low-Resource Language Neural Machine Translation Based on Transfer Learning and Dynamic Attention Enhancement

With the increasing pace of globalization, machine translation has become crucial for facilitating cross-cultural communication. However, translating low-resource languages remains challenging due to the scarcity of large parallel corpora. To address this issue, an innovative approach was introduced, combining transfer learning and a dynamic attention mechanism. This method enhances translation quality by utilizing high-resource language models to transfer knowledge and by dynamically concentrating on important elements of the input. In experiments with various low-resource language pairs, the proposed model demonstrated notable improvements, with BLEU score gains ranging from 1.30 to 4.44, validating the approach's effectiveness.