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公开(公告)号:US20210192150A1
公开(公告)日:2021-06-24
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 , G06F40/58 , G06F40/211
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:US20210192147A1
公开(公告)日:2021-06-24
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
IPC: G06F40/40
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:US20210192284A1
公开(公告)日:2021-06-24
申请号:US16901940
申请日:2020-06-15
Inventor: Hao XIONG , Zhongjun HE , Zhi LI , Hua WU , Haifeng WANG
IPC: G06K9/62 , G06F40/117
Abstract: The present disclosure provides an end-to-end model training method and apparatus, which relates to a field of artificial intelligence technologies. The method includes: obtaining training data containing a plurality of training samples, in which the plurality of training samples include an original sequence, a target sequence and a corresponding tag list, the tag list includes importance tags in the target sequence and avoidance tags corresponding to the importance tags, and the avoidance tags are irrelevant tags corresponding to the importance tags; and adopting the training data to train a preset end-to-end model until a value of a preset optimization target function is smaller than a preset threshold.
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