Evaluation of the Accuracy of Artificial Intelligence Translation Based on Deep Learning

Abstract

A machine automatic translation quality evaluation model based on a deep learning algorithm is built with the aim of achieving an accurate evaluation of machine automatic translation quality. Deep learning-based automatic machine translation was made. During the unsupervised and supervised learning stages, language information extraction executes unsupervised learning and reconstructs automated translation samples of bilingual words using a noise lessening automatic coding machine. To improve the effect of language vector feature extraction, the machine optimizes the effect of language vector feature extraction by importing language vector function and machine automatic translation information into bilingual words. The machine automatic translation language vector function is imported into the translation quality evaluation model based on deep learning to realize the automatic machine translation quality evaluation. The experimental results show that the constructed model can accurately evaluate the quality of machine automatic translation, the pattern of translated sentences and the number of sentences do not adversely affect the evaluation performance of the model, and the model exerts a good effect is showing.