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

A machine translation method, comprising:

• acquiring a source language text to be translated;
• and
• inputting the source language text into a preset machine translation model, and acquiring an output result of the preset machine translation model as a translation result of the source language text, wherein the preset machine translation model is trained by a model training method, and the model training method comprises:
• determining, through a neural network pruning technique, a respective influence degree of each parameter in a plurality of parameters in a first translation model on a translation result in a first field to obtain at least one first parameter and at least one second parameter, wherein an influence degree of a first parameter on the translation result in the first field is greater than an influence degree of a second parameter on the translation result in the first field, the first translation model is a generic translation model, and the first field is a generic field;
• training, by using a first corpus of the first field, the at least one first parameter of the first translation model to obtain a second translation model, wherein the at least one first parameter is updated and the at least one second parameter is unchanged during training, and similarity between a translation result of the second translation model in the first field and a translation result of the first translation model in the first field meets a preset condition;
• and
• training, by using a target corpus in a target field, a target parameter of the second translation model to obtain a target translation model, wherein the target parameter is updated and other parameters except the target parameter in the second translation model are unchanged during training, the target parameter is part or all of the at least one second parameter, and the target field is a specific field.

An electronic device, comprising:

• one or more processors;
• and
• a memory, which is configured to store one or more programs;
• wherein the one or more programs, when executed by the one or more processors, perform steps, wherein the steps comprise:
• determining, through a neural network pruning technique, a respective influence degree of each parameter in a plurality of parameters in a first translation model on a translation result in a first field to obtain at least one first parameter and at least one second parameter, wherein an influence degree of a first parameter on the translation result in the first field is greater than an influence degree of a second parameter on the translation result in the first field, the first translation model is a generic translation model, and the first field is a generic field;
• training, by using a first corpus of the first field, the at least one first parameter of the first translation model to obtain a second translation model, wherein the at least one first parameter is updated and the at least one second parameter is unchanged during training, and similarity between a translation result of the second translation model in the first field and a translation result of the first translation model in the first field meets a preset condition;
• and
• training, by using a target corpus in a target field, a target parameter of the second translation model to obtain a target translation model, wherein the target parameter is updated and other parameters except the target parameter in the second translation model are unchanged during training, the target parameter is part or all of the at least one second parameter, and the target field is a specific field.