| Title |
Optimization of Efficient English Phrase Translation System Based on BERT-BiLSTM and Genetic Algorithm |
| Authors |
(Jingyun Huang) ; (Bing Wang) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.245 |
| Keywords |
BERT-BiLSTM; Genetic algorithm; English translation; System optimization |
| Abstract |
In the era of deepening globalization, the demand for cross-language communication is increasing daily.
With the development of deep learning, neural network machine translation has become the mainstream. However, problems still need to be solved, such as low translation accuracy and limited ability to deal with complex syntactic structures. Given this, this paper proposes an English phrase translation system based on BERT-BiLSTM and genetic algorithm, which aims to solve the problems existing in the existing translation system and achieve high-efficiency and high-quality translation results. In this system, BERT is used for word vectorization processing, BiLSTM is used for sequence modeling, and a genetic algorithm is used to optimize the model parameters to obtain a better translation effect. The experiment found that the BLEU score was as high as 28.93 on the translation test set. The BLEU score increased by about 6% compared to the unoptimized traditional system. In addition, the system’s operating efficiency has also been significantly improved, with FLOPs reduced by about 7%. While maintaining high translation quality, the system can also effectively save computing resources and significantly improve translation efficiency. |