Research on the Application of Natural Language Processing Technology in English Translation Quality Control

Machine translation is an important research area in natural language processing. In this paper, we propose an innovative fusion translation model that combines the slicing method and the alignment method and select articles from different domains for experiments to explore the translation effects of the proposed method. The experiments show that the proposed CRF sequence annotation cut-scoring method performs excellently, with F-values of 86.7%, 89.0%, and 87.4% for the three domains of computer science, civil engineering, and medicine, respectively. The hybrid alignment method based on the length and lexical information also performs well, both in terms of correctness and recall and the hybrid alignment method is able to obtain better results than the length or lexical information methods alone. Putting the proposed method into the translation accuracy test, the BLEU score of the cut-and-align fusion translation model is improved to 11.08, while the NIST score is improved to 3.6468, which is a significant improvement in the translation quality of English.