A Study on the Influence Mechanism of English Corpus on Translation Quality in Multilingual Website Translation

Parallel corpora harbor rich bilingual correspondence resources, which are respected by scholars in assisting translation practice, in addition to being used in translation theory research and natural language processing. In this paper, we explore the influence of the English parallel corpus on the translation quality of multilingual websites using a mechanism that explores its influence mechanism. First, this paper proposes a neural machine translation model incorporating translation memory, and selects an English parallel corpus as the experimental corpus to evaluate the model’s effect on improving translation quality. Secondly, an English parallel corpus-assisted manual translation experiment was designed to analyze the effect of the English corpus on translation quality. In the six English parallel corpus translation directions, compared with the baseline model, the model construction method of this paper can effectively improve the BLEU scores, which is especially obvious in improving the translation quality of the RNN model, realizing an improvement of 2.91 BLEU scores in the English-German translation direction, which verifies the effectiveness of the method of this paper. In addition, the experimental group of the experimental English parallel corpus is not as good as the control group in terms of terminology and translation efficiency, but significantly better than the control group in terms of the quality of terminology translation. Therefore, the proposed NMT modeling method can be combined with the English corpus to achieve unification of translation quality and efficiency on multilingual websites.