Model training method, machine translation method, computer device, and storage medium

A machine translation method, comprising:

• obtaining to-be-translated content;
• obtaining a translation result corresponding to the to-be-translated content by using a machine translation model, the machine translation model being obtained by performing training according to the model training method comprising:
• obtaining a training sample set, the training sample set comprising a training sample and a standard tag vector corresponding to the training sample;
• inputting the training sample into a plurality of attention networks of the machine translation model, and performing nonlinear transformation on respective output vectors of the plurality of attention networks;
• obtaining a feature fusion vector corresponding to the plurality of attention networks;
• obtaining a predicted tag vector outputted by the machine translation model according to the feature fusion vector, the predicted tag vector corresponding to the training sample;
• performing iteration adjustment on a model parameter of the machine translation model according to a result of a comparison between the predicted tag vector and the standard tag vector corresponding to the training sample;
• and
• in response to the neural network model meeting a convergence condition, identifying the machine translation model;
• and
• displaying the translation result.

A computer device, comprising a processor and a memory;

• the memory being configured to store program code executable by the processor;
• and
• the processor being configured to execute the program code and perform a plurality of operations comprising:
• obtaining a training sample set, the training sample set comprising a training sample and a standard tag vector corresponding to the training sample;
• inputting the training sample in the training sample set into a plurality of attention networks of a neural network model, and performing nonlinear transformation on respective output vectors of the plurality of attention networks by using the neural network model, to obtain a feature fusion vector corresponding to the plurality of attention networks;
• obtaining a predicted tag vector outputted by the neural network model according to the feature fusion vector, the predicted tag vector corresponding to the training sample;
• and
• performing iteration adjustment on a model parameter of the neural network model according to a result of comparison between the predicted tag vector corresponding to the training sample and the standard tag vector;
• in response to the neural network model meeting a convergence condition, identifying a target neural network model.