On Applying Natural Language Processing Technology to Optimize Accuracy of English Interactive Translation Platform

Abstract

Abstract This research establishes a phrase-based statistical machine translation platform composed of multiple functional modules, including data preprocessing, word alignment, translation model training, ordering model training, language model training, decoding, and parameter optimization. Special emphasis is placed on the quality of bilingual word alignment, utilizing both the IBM model and the statistical collocation model to enhance alignment accuracy. The study reveals that this new platform significantly enhances translation efficiency across diverse corpora, such as legal documents, news articles, and parliamentary records. Notably, the translation of parliamentary records demonstrates a remarkable reduction in average translation time by 27.53% and a decrease in the number of decoding iterations by 56.46% when compared to System1 using the Baseline system. Furthermore, a BLEU-based evaluation indicates that the Baseline system consistently surpasses the comparison system in translating various text types. This study underscores the practical effectiveness of the developed translation platform and its contribution to the advancement of natural language processing technology.