公开(公告)号：US20210200963A1

公开(公告)日：2021-07-01

申请号：US17200588

申请日：2021-03-12

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Ruiqing Zhang ,  Chuanqiang Zhang ,  Jiqiang Liu ,  Zhongjun He ,  Zhi Li ,  Hua Wu

IPC: G06F40/47 ,  G06F40/58 ,  G06F40/45

Abstract: The present disclosure provides a machine translation model training method, apparatus, electronic device and storage medium, which relates to the technical field of natural language processing. A specific implementation solution is as follows: 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 training method according to the present disclosure is time-saving and effort-saving, and may effectively improve the training efficiency of the machine translation model in the target field.