Machine translation model training method, apparatus, electronic device and storage medium

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method for training a machine translation model in a target field, wherein the method comprises:
• selecting, from parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which have universal-field features and/or target-field features, to constitute a first training sample set;
• selecting, from the parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which do not have universal-field features and target-field features, to constitute a second training sample set;
• training an encoder in the machine translation model in the target field, a discriminator configured in encoding layers of the encoder, and the encoder and a decoder in the machine translation model in the target field in turn with the first training sample set and second training sample set, respectively;
• the discriminator being used to recognize fields to which input samples during training belong.

A non-transitory computer readable storage medium with computer instructions stored thereon, wherein the computer instructions are used for causing a computer to perform a method for training a machine translation model in a target field, wherein the method comprises:

• selecting, from parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which have universal-field features and/or target-field features, to constitute a first training sample set;
• selecting, from the parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which do not have universal-field features and target-field features, to constitute a second training sample set;
• training an encoder in the machine translation model in the target field, a discriminator configured in encoding layers of the encoder, and the encoder and a decoder in the machine translation model in the target field in turn with the first training sample set and second training sample set, respectively;
• the discriminator being used to recognize fields to which input samples during training belong.