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    METHOD AND APPARATUS FOR ESTABLISHING RISK PREDICTION MODEL AS WELL AS REGIONAL RISK PREDICTION METHOD AND APPARATUS
    4.
    发明申请
    METHOD AND APPARATUS FOR ESTABLISHING RISK PREDICTION MODEL AS WELL AS REGIONAL RISK PREDICTION METHOD AND APPARATUS 有权

    公开(公告)号：US20220398465A1

    公开(公告)日：2022-12-15

    申请号：US17620820

    申请日：2021-06-02

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Jizhou HUANG ,  Jingbo ZHOU ,  An ZHUO ,  Ji LIU ,  Haoyi XIONG ,  Dejing DOU ,  Haifeng WANG

    IPC: G06N5/02

    Abstract: A technical solution relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process. The encoder performs a coding operation using region features of the sample regions to obtains a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region.

    METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM FOR TRAINING TEXT GENERATION MODEL
    5.
    发明申请
    METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM FOR TRAINING TEXT GENERATION MODEL 有权

    公开(公告)号：US20210374359A1

    公开(公告)日：2021-12-02

    申请号：US17133381

    申请日：2020-12-23

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Wei LI ,  Xinyan XIAO ,  Hua WU ,  Haifeng WANG

    IPC: G06F40/56 ,  G06F40/58

    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.

    END-TO-END MODEL TRAINING METHOD AND APPARATUS, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
    6.
    发明申请
    END-TO-END MODEL TRAINING METHOD AND APPARATUS, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM 有权

    公开(公告)号：US20210192284A1

    公开(公告)日：2021-06-24

    申请号：US16901940

    申请日：2020-06-15

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    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.

    METHOD, APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM FOR PROCESSING A SEMANTIC REPRESENTATION MODEL
    7.
    发明申请
    METHOD, APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM FOR PROCESSING A SEMANTIC REPRESENTATION MODEL 有权

    公开(公告)号：US20210182498A1

    公开(公告)日：2021-06-17

    申请号：US16885358

    申请日：2020-05-28

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Yu SUN ,  Haifeng WANG ,  Shuohuan WANG ,  Yukun LI ,  Shikun FENG ,  Hao TIAN ,  Hua WU

    IPC: G06F40/30 ,  G06F40/40

    Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.

    METHOD OF TRAINING A DESCRIPTIVE TEXT GENERATING MODEL, AND METHOD AND APPARATUS FOR GENERATING DESCRIPTIVE TEXT
    8.
    发明申请
    METHOD OF TRAINING A DESCRIPTIVE TEXT GENERATING MODEL, AND METHOD AND APPARATUS FOR GENERATING DESCRIPTIVE TEXT 审中-公开

    公开(公告)号：US20190384810A1

    公开(公告)日：2019-12-19

    申请号：US16176783

    申请日：2018-10-31

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Jizhou HUANG ,  Yaming SUN ,  Wei ZHANG ,  Haifeng WANG

    IPC: G06F17/27 ,  G06N7/00 ,  G06K9/00 ,  G06K9/62 ,  G06F17/30

    Abstract: The present disclosure provides a method of training a descriptive text generating model, and a method and apparatus for generating a descriptive text, wherein the method of training a descriptive text generating model comprises: obtaining training data, the training data comprising: a notional word, a first descriptive text and a second descriptive text of the notional word, wherein the second descriptive text is a concise expression of the first descriptive text; regarding the notional word and the first descriptive text of the notional word as input of a seq2seq model, regarding the second descriptive text of the notional word as output of the seq2sequ model, and training the seq2seq model to obtain a descriptive text generating model. The descriptive text generating model according to the present disclosure can implement generation of a concise descriptive text with respect to the notional word in a deep understanding manner.

    MULTI-LINGUAL MODEL TRAINING METHOD, APPARATUS, ELECTRONIC DEVICE AND READABLE STORAGE MEDIUM
    9.
    发明申请
    MULTI-LINGUAL MODEL TRAINING METHOD, APPARATUS, ELECTRONIC DEVICE AND READABLE STORAGE MEDIUM 有权

    公开(公告)号：US20220171941A1

    公开(公告)日：2022-06-02

    申请号：US17348104

    申请日：2021-06-15

    Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

    Inventor： Xuan OUYANG ,  Shuohuan WANG ,  Chao PANG ,  Yu SUN ,  Hao TIAN ,  Hua WU ,  Haifeng WANG

    IPC: G06F40/30 ,  G06F40/58 ,  G06N20/00

    Abstract: The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge. In the present disclosure, the multi-lingual model can be enabled to achieve semantic interaction between different languages and improve the accuracy of the multi-lingual model in learning the semantic representations of the multi-lingual model.

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