LAUSR

This paper adopts the algorithm of the deep neural network to conduct in-depth research and analysis on the factors associated with the improvement of English translation ability. This study focuses on text complexity, adding discourse complexity features in addition to focusing on lexical and syntactic dimensions, exploring the application of neural network algorithm in the construction of text complexity grading model based on feature optimization, and examining the performance and generalization ability of the model. The rationality of the grading of the material is verified. After determining the model input features and training corpus, different classification algorithms were used to build the models and compare their performance. Meanwhile, compared with the models constructed based on common traditional readability formulas and other single-dimensional features, the models constructed based on the feature set of this study have significant advantages, with 20 to 30 percentage points higher in each performance evaluation index. The pseudo-parallel corpus is constructed, back translation is performed after obtaining the pseudo-parallel corpus, and finally, the data migration effect is measured and recorded on the low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, and the cycle continues until the model performance is no longer improved. The low-resource neural machine translation model based on model migration learning improved 3.97 and 2.64 BLEU values in the low-resource English translation task, respectively, and reduced the training time; based on this, the data migration learning method further improved 2.26 and 2.52 BLEU values.