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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：US20210248498A1
公开(公告)日：2021-08-12
申请号：US17241999
申请日：2021-04-27
Inventor： Chao PANG , Shuohuan WANG , Yu SUN , Zhi LI
Abstract: A method for training a pre-trained knowledge model includes: obtaining a training text, in which the training text includes a structured knowledge text and an article corresponding to the structured knowledge text, and the structured knowledge text includes a head node, a tail node, and a relationship between the head node and the tail node; and training a pre-trained knowledge model to be trained according to the training text.
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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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公开(公告)号：US20210397780A1
公开(公告)日：2021-12-23
申请号：US17405813
申请日：2021-08-18
Inventor： Chao PANG , Shuohuan WANG , Yu SUN , Zhi LI
IPC: G06F40/166 , G06K9/46 , G06K9/62 , G06N20/00
Abstract: A method for correcting an error in a text, an electronic device, and a storage medium are provided. The method includes: obtaining an original text; obtaining a training text by preprocessing the original text; extracting a plurality of feature vectors corresponding to each word in the training text; obtaining an input vector by processing the plurality of feature vectors; obtaining a target text by inputting the input vector into a text error correction model; and adjusting parameters of the text error correction model based on a difference between the target text and the original text.
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Patent Agency Ranking
6.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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7.发明申请公开(公告)号：US20210192141A1
公开(公告)日：2021-06-24
申请号：US16939947
申请日：2020-07-27
Inventor： Chao PANG , Shuohuan WANG , Yu SUN , Zhi LI
Abstract: A method for generating a vector representation of a text includes dividing the text into text segments. Each text segment is represented as a segment vector corresponding to the respective text segment by employing a first-level semantic model. The segment vector is configured to indicate a semantics of the text segment. Text semantics recognition is performed on the segment vector of each text segment by employing a second-level semantic model to obtain a text vector for indicating a topic of the text.
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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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