Treffer: Algorithm-driven Systematic Approach to English Translation and its Impact on Foreign Language Teaching.

English translation performs a pivotal part in the context of foreign language teaching (FLT), serving as a bridge and a tool for understanding and communication across cultures. As English has become a globally spoken language, mastering its translation has become essential for educators and students alike. English translation enhances FLT by enhancing students’ understanding of syntax, vocabulary, and cultural nuances, enabling them to effectively connect their native language with English. This research investigates an algorithm-driven systematic approach to English translation and its impact on FLT. To investigate the English–Chinese translation accuracy, this research utilized a sample of Chinese text data. These translated text data were preprocessed by subword byte-pair tokenization (SBPT). The translation approach uses deep learning, such as the manta-ray foraging mutated efficient recurrent neural network (MRF-ERNN) model, to improve the accuracy of on-demand translations and language understanding. Understanding intricate linguistic structures, vocabulary, and cultural quirks is the main objective of the research, which examines English translation and affects foreign language instruction. There are two recurring stages to the process: The first stage evaluates the word placement and understandability, and the second makes sure that the desired message is effectively communicated. While student translations were nearly equivalent to machine translations in accuracy (96%), contextual relevance (95.8%), and fluency (94.3%), the MRF-ERNN optimization greatly enhanced translation quality, as seen by BLEU rising from 0.61 to 0.93 and recall rising from 0.68 to 0.93. These findings demonstrate how MRF-ERNN could enhance learning outcomes and translation accuracy. [ABSTRACT FROM AUTHOR]

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