Semantic relationship extraction of English long sentences and quality optimization of machine translation based on BERT model

Abstract

With the acceleration of globalization, cross-linguistic communication has become an indispensable part of daily life, and the status of English as an international lingua franca has become increasingly prominent. Faced with the complex semantic relations contained in long English sentences, the existing machine translation systems often show understanding deviations and translation distortions, which seriously affect the accuracy and coherence of information transmission. To solve this pain point, this study focuses on the latest achievement in the field of deep learning models and explores its application potential in English long sentence semantic relationship extraction and machine translation quality optimization. Firstly, the BERT model is fine-tuned to specialize in long sentence structure analysis and semantic relationship extraction. Experiments show that the F1 score of the model reaches 89.6% on the standard evaluation dataset CoNLL 2004, which is significantly higher than the previous best record. Based on this deeply mined semantic information, we further optimize the neural network machine translation system to effectively solve the long-distance dependency problem and significantly reduce the ambiguity and omission phenomenon in the translation process by introducing a novel attention guidance mechanism. In the blind test of the WMT ‘14 English-German translation task, the BLEU score of the translated version using the optimized NMT system is 29.5, which is 3.2 points higher than that of the benchmark model, which proves the remarkable effect of this method in improving translation quality.