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公开(公告)号：US11977850B2
公开(公告)日：2024-05-07
申请号：US17411917
申请日：2021-08-25
Inventor： Fan Wang , Siqi Bao , Huang He , Hua Wu , Jingzhou He , Haifeng Wang
CPC classification number: G06F40/35 , G06F16/325 , G06F16/3347 , G06F18/285 , G06F40/30 , G10L15/01 , G10L15/18 , G10L15/22
Abstract: A method for dialogue processing, an electronic device and a storage medium are provided. The specific technical solution includes: obtaining a dialogue history; selecting a target machine from a plurality of machines; inputting the dialogue history into a trained dialogue model in the target machine to generate a response to the dialogue history, in which the dialogue model comprises a common parameter and a specific parameter, and different machines correspond to the same common parameter.
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公开(公告)号：US11847150B2
公开(公告)日：2023-12-19
申请号：US17407320
申请日：2021-08-20
Inventor： Yuchen Ding , Yingqi Qu , Jing Liu , Kai Liu , Dou Hong , Hua Wu , Haifeng Wang
CPC classification number: G06F16/3347 , G06F16/3344 , G06N20/20
Abstract: The present application discloses a method and apparatus for training a retrieval model, device and computer storage medium that relate to intelligent search and natural language processing technologies. An implementation includes: acquiring initial training data; performing a training operation using the initial training data to obtain an initial retrieval model; selecting texts with the correlation degrees with a query in the training data meeting a preset first requirement from candidate texts using the initial retrieval model; performing a training operation using the updated training data to obtain a first retrieval model; and selecting texts with the correlation degrees with the query in the training data meeting a preset second requirement from the candidate texts using the first retrieval model; and/or selecting texts with the correlation degrees with the query meeting a preset third requirement; and performing a training operation using the expanded training data to obtain a second retrieval model.
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公开(公告)号：US11663404B2
公开(公告)日：2023-05-30
申请号：US17101789
申请日：2020-11-23
Inventor： Shuohuan Wang , Siyu Ding , Yu Sun , Hua Wu , Haifeng Wang
IPC: G06F40/279 , G06N20/00 , G06F40/166 , G06F40/30
CPC classification number: G06F40/279 , G06F40/166 , G06F40/30 , G06N20/00
Abstract: The disclosure provides a text recognition method, an electronic device, and a storage medium. The method includes: obtaining N segments of a sample text; inputting each of the N segments into a preset initial language model in sequence, to obtain first text vector information corresponding to the N segments; inputting each of the N segments into the initial language model in sequence again, to obtain second text vector information corresponding to a currently input segment; in response to determining that the currently input segment has the mask, predicting the mask according to the second text vector information and the first text vector information to obtain a predicted word at a target position corresponding to the mask; training the initial language model according to an original word and the predicted word to generate a long text language model; and recognizing an input text through the long text language model.
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公开(公告)号：US11574133B2
公开(公告)日：2023-02-07
申请号：US17133381
申请日：2020-12-23
Inventor： Wei Li , Xinyan Xiao , Hua Wu , Haifeng Wang
Abstract: The disclosure may provide a method for obtaining a document layout, an electronic device, and a storage medium. The method may include: obtaining a plurality of pieces of first sample data; extracting structured information from each of the plurality of pieces of first sample data as target structured information corresponding to each of the plurality of pieces of first sample data; inputting the plurality of pieces of first sample data into an initial text generation model to generate predicted structured information corresponding to each of the plurality of pieces of first sample data; generating a first loss value based on a difference between the predicted structured information corresponding to each of the plurality of pieces of first sample data and the corresponding target structured information; and training a phrase generation ability of the initial text generation model based on the first loss value to generate the text generation model.
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35.发明授权公开(公告)号：US11531813B2
公开(公告)日：2022-12-20
申请号：US17015411
申请日：2020-09-09
Inventor： Xinchao Xu , Haifeng Wang , Hua Wu , Zhanyi Liu
IPC: G06F40/284 , G06F40/274 , G06N3/08 , G10L15/04 , G10L15/06
Abstract: A method, an electronic device and a readable storage medium for creating a label marking model are disclosed. The method for creating the label marking model includes: obtaining text data and determining a word or phrase to be marked in the text data; according to the word or phrase to be marked, constructing a first training sample of the text data corresponding to a word or phrase replacing task and a second training sample corresponding to a label marking task; training a neural network model with a plurality of the first training samples and a plurality of the second training samples, respectively, until a loss function of the word or phrase replacing task and a loss function of the label marking task satisfy a preset condition, to obtain the label marking model.
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Patent Agency Ranking
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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37.发明申请公开(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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公开(公告)号：US20210280190A1
公开(公告)日：2021-09-09
申请号：US17327706
申请日：2021-05-22
Inventor： Wenquan Wu , Hua Wu , Haifeng Wang
Abstract: A method and apparatus for human-machine interaction, a device, and a medium are provided. A specific implementation solution is: generating reply text of a reply to a received speech signal based on the speech signal; generating a reply speech signal corresponding to the reply text based on a mapping relationship between a speech signal unit and a text unit, the reply text including a group of text units; determining an identifier of an expression and/or action based on the reply text, the expression and/or action being presented by a virtual object; and generating an output video including the virtual object based on the reply speech signal and the identifier of the expression and/or action, the output video including a lip shape sequence determined based on the reply speech signal and to be presented by the virtual object.
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公开(公告)号：US20210280189A1
公开(公告)日：2021-09-09
申请号：US17326917
申请日：2021-05-21
Inventor： Jun Xu , Zeyang Lei , Zhengyu Niu , Hua Wu , Haifeng Wang
Abstract: A first expression corresponding to an input statement is obtained from a conversational graph. The conversational graph includes expressions having association relationships therebetween and conversation target clusters having association relationships therebetween. Each conversation target cluster includes at least two expressions. A second expression associated with the first expression is obtained from the conversational graph based on the association relationships of expressions and the association relationships of conversational target clusters in the conversational graph. A reply statement is generated based on the second expression and the input statement.
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公开(公告)号：US20210200813A1
公开(公告)日：2021-07-01
申请号：US16986631
申请日：2020-08-06
Inventor： Jun Xu , Zeyang Lei , Zhengyu Niu , Hua Wu , Haifeng Wang
IPC: G06F16/9032 , G06F16/903 , G06F40/35 , G06F16/901
Abstract: A human-machine interaction method is related to the field of artificial intelligence technologies. The method includes: obtaining a conversation sentence input by a user; obtaining a query sentence matching the conversation sentence; obtaining a plurality of associated query sentences corresponding to the query sentence based on a preset query word graph; processing the conversation sentence and the plurality of associated query sentences through a preset algorithm to select a target query sentence from the plurality of associated query sentences; and processing the target query sentence based on a preset response generation model to generate a response sentence for the user.
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