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公开(公告)号：US11704498B2
公开(公告)日：2023-07-18
申请号：US17200551
申请日：2021-03-12
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Zhongjun He , Zhi Li , Hua Wu
IPC: G06F40/30 , G06F40/51 , G06F40/44 , G06F40/49 , G06F18/214
CPC classification number: G06F40/30 , G06F18/214 , G06F40/44 , G06F40/49 , G06F40/51
Abstract: A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second training sample set.
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Patent Agency Ranking
公开(公告)号：US11423222B2
公开(公告)日：2022-08-23
申请号：US17243097
申请日：2021-04-28
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Zhongjun He , Zhi Li , Hua Wu
IPC: G06F40/232 , G06N20/00 , G06F40/279 , G06F40/166
Abstract: A method for text error correction includes: obtaining a text to be corrected; obtaining a pinyin sequence of the text to be corrected; and inputting the text to be corrected and the pinyin sequence to a text error correction model, to obtain a corrected text.
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3.发明申请公开(公告)号：US20210200963A1
公开(公告)日：2021-07-01
申请号：US17200588
申请日：2021-03-12
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Jiqiang Liu , Zhongjun He , Zhi Li , Hua Wu
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.
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公开(公告)号：US11275904B2
公开(公告)日：2022-03-15
申请号：US16868426
申请日：2020-05-06
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Hao Xiong , Zhongjun He , Hua Wu , Zhi Li , Haifeng Wang
Abstract: Embodiments of the present disclosure provide a method and an apparatus for translating a polysemy, and a medium. The method includes: obtaining a source language text; identifying and obtaining the polysemy from the source language text; inquiring related words corresponding to each interpretation of the polysemy; determining a target interpretation corresponding to the related words contained in the source language text; and translating the polysemy into the target interpretation.
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公开(公告)号：US11409968B2
公开(公告)日：2022-08-09
申请号：US16926197
申请日：2020-07-10
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Hao Xiong , Zhongjun He , Hua Wu , Haifeng Wang
IPC: G06F40/55 , G06F40/211 , G06F40/58
Abstract: Embodiments of the present disclosure provide a language conversion method and apparatus based on syntactic linearity and a non-transitory computer-readable storage medium. The method includes: encoding a source sentence to be converted by using a preset encoder to determine a first vector and a second vector corresponding to the source sentence; determining a current mask vector according to a preset rule, in which the mask vector is configured to modify vectors output by the preset encoder; determining a third vector according to target language characters corresponding to source characters located before a first source character; and decoding the first vector, the second vector, the mask vector, and the third vector by using a preset decoder to generate a target character corresponding to the first source character.
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公开(公告)号：US20210390266A1
公开(公告)日：2021-12-16
申请号：US17200551
申请日：2021-03-12
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Zhongjun He , Zhi Li , Hua Wu
Abstract: A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second sample training set. With the above-mentioned technical solution of the present application, by training the two models jointly, while the semantic similarity model is trained, the machine translation model may be optimized and nurtures the semantic similarity model, thus further improving the accuracy of the semantic similarity model.
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