Research of improvement of multilingual scientific translation model based on neural network attention mechanism

The application of neural network algorithms for multilingual translation is currently an important research field. Traditional sequence neural frameworks, such as RNN and its variant LSTM and GRU models, slow the training and inference speed, meanwhile, lower the accuracy of output translations when processing long sequences, as their inherent sequential processing mechanism limits the possibility of parallel processing. This report addresses the shortcomings of traditional sequential neural frameworks and establishes a transformer model that includes an attention mechanism encoder and decoder to make up the short brand. It combines self-attention with neural networks and systematically implements multi-lingual scientific translation to improve translation optimality on the basis of PyTorch. The experimental test results indicate that the BLUE value of the transformer model with attention mechanism on the field of scientific translation is improved to varying degrees compared with that of traditional sequential neural algorithms. This proves that the performance of the transformer algorithm model with attention mechanism is significantly better than that of traditional models and put forward improved suggestions on the basis of this qualified transformer algorithm model with attention mechanism.