A study of an intelligent algorithm combining semantic environments for the translation of complex English sentences

Abstract

Abstract In order to improve the translation quality of complex English sentences, this paper investigated unknown words. First, two baseline models, the recurrent neural machine translation (RNMT) model and the transformer model, were briefly introduced. Then, the unknown words were identified and replaced based on WordNet and the semantic environment and input to the neural machine translation (NMT) model for translation. Finally, experiments were conducted on several National Institute of Standards and Technology (NIST) datasets. It was found that the transformer model significantly outperformed the RNMT model, its average bilingual evaluation understudy (BLEU) value was 42.14, which was 6.96 higher than the RNMT model, and its translation error rate (TER) was also smaller. After combining the intelligent algorithm, the BLEU values of both models improved, and the TER became smaller; the average BLEU value of the transformer model combined with the intelligent algorithm was 43.7, and the average TER was 57.68. The experiment verifies that the transformer model combined with the intelligent algorithm is reliable in translating complex sentences and can obtain higher-quality translation results.