Machine Translation-Based Language Modeling Enables Multi-Scenario Applications of English Language

Traditional machine translation models suffer from problems such as long training time and insufficient adaptability when dealing with multiple English language scenarios. At the same time, some models often struggle to meet practical translation needs in complex language environments. A translation model that combines the feed-forward neural network decoder and the attention mechanism is suggested as a solution to this problem. Additionally, the model analyzes the similarity of the English language to enhance its translation ability. The resulting machine translation model can be applied to different English scenarios. The study's findings showed that the model performs better when the convolutional and attention layers have a higher number of layers relative to one another. The highest average value of the bilingual evaluation study for the research use model was 29.65. The research use model can machine translate different English language application scenarios and also the model performed better. The new model performed better than the traditional model and was able to translate the English language well in a variety of settings. The model used in the study had the maximum parameter data size of 4586, which is 932 higher than the lowest statistical machine translation model of 3654. The metric value was 3.96 higher than the statistical machine translation model. It is evident that investigating the use of the model can enhance the English language scene translation effect, with each scene doing well in translation. This provides new ideas for the direction of multi-scene application of machine translation language model afterwards.