Cross-border English text semantic alignment technology optimized by BERT-INT8 quantitative model

Abstract

With the in-depth development of globalization, semantic alignment technology of cross-border English texts plays an increasingly important role in multilingual information processing. However, traditional semantic alignment methods are often limited by model size and computational efficiency, and it isn’t easy to meet the dual requirements of real-time and accuracy. This study aims to improve the performance of cross-border English text semantic alignment through BERT-INT8 quantitative model. We first perform INT8 quantization processing on the BERT model, significantly reducing the model parameters and computational complexity. The experimental results show that the semantic alignment accuracy of the optimized model has improved by 5.2% to 92.7%. The processing speed has been increased by 1.8 times, reaching the speed of processing 3000-word pairs per second. The average alignment accuracy on different types of text reached 90.5%, which was 4.3 percentage points higher than that of the unoptimized model. These results show that the BERT-INT8 quantization model significantly improves the processing efficiency while maintaining high alignment accuracy, with strong generalization ability and robustness. This study provides a new technical path for fast and accurate alignment of cross-border English texts, which can play an important role in practical applications.