Research on Situational English Translation for Intelligent Software Engineering Based on Attention Mechanism and Bidirectional LSTM Coding Models
In order to effectively improve the performance of the translation model for situational English translation for intelligent software engineering and its translation effect,this research combines the Attention mechanism and bidirectional long and short-term memory network to build a new English translation model,based on which,the situational English corpus for intelligent software engi-neering is designed by combining various software and hardware devices.The results of the study show that the constructed translation model has better performance,can reach a stable state after 20 iterations at most,and its running error value is kept below 0.05,and the translation accuracy rate is as high as 0.98.In the practical application,it can also obtain higher expert satisfaction.In conclu-sion,the translation model and English corpus constructed in this research can provide new technical support for software engineering situational English translation,and promote English translation to be smarter and more situationalized.
万方数据
维普
