Optimization of Neural Machine Translation Model Based on Continuous Bag of Words
In the context of globalization,machine translation models are of great significance in promoting cultural exchange a-mong countries.A neural machine translation model based on continuous word bag model and improved long short-term memory net-work was studied and constructed to address the issues of mistranslation and omission in neural machine translation models.The results indicate that the bilingual evaluation substitute value of the proposed model can reach 39.49%in the English French dataset.The ac-curacy rates for translating from French,German,and Spanish into English were 79.2%,75.1%,and 76.7%,respectively.The ac-curacy rates for translating from English to other languages were 79.8%,77.6%,and 78.7%,respectively.The research results show that,the proposed model has high translation performance and accuracy,can effectively optimize the Transformer neural machine translation model,and has good adaptability in translation tasks in different languages.It has certain feasibility and practical applica-tion prospects.Research can provide certain technical support for English translation,promote cross-cultural communication,and deepen cross-border trade.
neural machine translationcontinuous word bag modellong short-term memory networkEnglish translation
万方数据
维普
