Design and Construction of Machine Translation System Based on RNN Model
Xiujie Peng · 2023
With the development of artificial intelligence technology, the application of deep learning algorithm makes adaptive multilingual machine translation possible. Under the conventional “encoder-decoder” framework, many machine translation models have good practical effects, but there are also obvious shortcomings. In this regard, this paper takes the field of multilingual translation as the research object, constructs a machine translation model based on the recurrent neural network (RNN), and puts forward attention mechanisms for semantic mining and long text sequence translation to optimize the practical application effect of the machine translation model. Finally, the model is integrated and sealed and deployed on the Web server, which is convenient for users to use online through remote login. The experimental results show that the RNN machine translation model with improved attention mechanism can significantly improve the translation effect under test data sets such as WMT17 and newsdev, and the BLEU value is increased by 29.33%, which effectively improves the adaptability and effectiveness of the machine translation system.