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1.发明申请公开(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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公开(公告)号：US20220180058A1
公开(公告)日：2022-06-09
申请号：US17383611
申请日：2021-07-23
Inventor： Ruiqing ZHANG , Chuanqiang ZHANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/232 , G06F40/40 , G06F40/253
Abstract: The present disclosure provides a text error correction method, apparatus, electronic device and storage medium, and relates to the technical field of artificial intelligence such as natural language processing and deep learning. A specific implementation solution is: obtaining a current sentence and a historical sentence of the current sentence in an article to which the current sentence belongs; performing text error correction processing on the current sentence based on the current sentence and the historical sentence. According to the technical solutions of the present disclosure, text error correction can be performed on the current sentence based on the historical sentence, namely, the upper contextual information, of the current sentence in the article, so that the error correction information is richer and the error correction result is more accurate.
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公开(公告)号：US20210248309A1
公开(公告)日：2021-08-12
申请号：US17243097
申请日：2021-04-28
Inventor： Ruiqing ZHANG , Chuanqiang ZHANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/166 , G06F40/279 , G06N20/00
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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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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Patent Agency Ranking
公开(公告)号：US20180165278A1
公开(公告)日：2018-06-14
申请号：US15832013
申请日：2017-12-05
Inventor： Zhongjun HE , Hongyu Liu , Shiqi Zhao , Hua Wu
CPC classification number: G06F17/289 , G06F17/2818 , G06F17/2845 , G06F17/2872 , G06N3/02 , G06N3/0445 , G06N3/0472 , G06N3/08
Abstract: The resent disclosure provides a method and an apparatus for translating based on artificial intelligence. With the method, the text to be translated from the source language to the target language is acquired, in which, the text includes the target language term and the source language term. The candidate terms for translating the source language term and confidences of the candidate terms are determined. The candidate terms are used to replace the corresponding source language term, and each candidate term is combined with the target language term, so as to obtain each candidate translation. A probability of forming a smooth text when the candidate term is used in the candidate translation is predicted. Then the target term is chosen to be recommended according to the language probabilities of the candidate translations and the confidences of the candidate terms.
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公开(公告)号：US20240220812A1
公开(公告)日：2024-07-04
申请号：US17474950
申请日：2021-09-14
Inventor： Ruiqing ZHANG , Xiyang WANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06N3/094
CPC classification number: G06N3/094
Abstract: A method for training a machine translation (MT) model and an electronic device are provided. The technical solution includes: obtaining an original training sample configured to train an MT model; generating at least one adversarial training sample of the MT model based on the original training sample; training the MT model based on the original training sample and the adversarial training sample.
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公开(公告)号：US20230342560A1
公开(公告)日：2023-10-26
申请号：US18121351
申请日：2023-03-14
Inventor： Ruiqing ZHANG , Hui LIU , Xiyang WANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/47 , G06F40/166 , G06F40/30
CPC classification number: G06F40/47 , G06F40/166 , G06F40/30 , G06F40/247
Abstract: A text translation method is described that includes initially acquiring text. Thereafter, first text is determined in the initial text; and second text is determined according to the first text, where the second text is used for describing the first text. Additionally, initial text is translated to obtain initial translation text, and the second text is translated to obtain description translation text. Thereafter, the initial translation text is updated according to the description translation text to obtain target translation text of the initial text.
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公开(公告)号：US20210326538A1
公开(公告)日：2021-10-21
申请号：US17362628
申请日：2021-06-29
Inventor： Chuanqiang ZHANG , Ruiqing ZHANG , Zhi LI , Zhongjun HE , Hua WU
Abstract: A method for text translation includes obtaining a text to be translated; and inputting the text to be translated into a text translation model. The trained text translation model divides the text to be translated into a plurality of semantic units, determines N semantic units before a current semantic unit among the plurality of semantic units as local context semantic units, determines M semantic units before the local context semantic units as global context semantic units, and generates a translation result of the current semantic unit based on the local context semantic units and the global context semantic units. N is an integer, and M is an integer.
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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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