Research on automatic machine translation system based on Markov tree temporal annotation algorithm
In the context of increasingly frequent cultural exchanges between Chinese and English,accurate Chinese-English ma-chine translation has become an important issue.To solve the problem of inconsistent English tenses,a Markov tree tenses annotation algorithm is proposed,and based on this,an automatic machine translation system is constructed by combining the deep learning Transformer model.The results showed that in the comparison of temporal and non-temporal data,the overall accuracy of temporal annotation increased from 0.670 to 0.720,and the accuracy of verb increased from 0.676 to 0.725.In addition,the tagging accuracy of new words increased from 64.9％to 84.9％for binary structures and from 61.2％to 92.9％for ternary structures.In addition,the experimental results show that the automatic translation system combined with temporal annotation has a higher translation accuracy than the baseline model without temporal annotation.This research is of great significance to improve the accuracy and reliability of machine translation,and provides a valuable direction for the further development of machine translation technology.
万方数据
维普
