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    TRANSLATION METHOD, CLASSIFICATION MODEL TRAINING METHOD, DEVICE AND STORAGE MEDIUM
    1.
    发明申请
    TRANSLATION METHOD, CLASSIFICATION MODEL TRAINING METHOD, DEVICE AND STORAGE MEDIUM 有权

    公开(公告)号：US20230015313A1

    公开(公告)日：2023-01-19

    申请号：US17656160

    申请日：2022-03-23

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

    Inventor： Chuanqiang Zhang ,  Ruiqing Zhang ,  Zhongjun He ,  Zhi Li ,  Hua Wu

    IPC: G06F40/58 ,  G06F40/279

    Abstract: Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

    LARGE LANGUAGE MODEL TRAINING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    3.
    发明申请
    LARGE LANGUAGE MODEL TRAINING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权

    公开(公告)号：US20250094806A1

    公开(公告)日：2025-03-20

    申请号：US18967167

    申请日：2024-12-03

    Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

    Inventor： Junyuan Shang ,  Yilong Chen ,  Zhenyu Zhang ,  Shuohuan Wang ,  Yu Sun ,  Hua Wu

    IPC: G06N3/082 ,  G06N3/0475

    Abstract: Provided is a large language model training method, an electronic device and a storage medium, relating to the field of artificial intelligence technologies, and in particular, to the fields of deep learning, natural language processing and large model. The method includes: performing dimension reduction parameter fusion on a two-dimensional parameter matrix on each channel in each network layer in a first large language model, respectively, to obtain a second large language model; performing layer reduction parameter fusion on network layers in the second large language model based on a three-dimensional parameter matrix of each network layer in the second large language model to obtain a third large language model; and training the third large language model to obtain a target large language model under the condition that the target loss function determined based on the first and third large language models meets a preset first function condition.

    DIALOG METHOD AND SYSTEM, ELECTRONIC DEVICE AND STORAGE MEDIUM
    6.
    发明申请
    DIALOG METHOD AND SYSTEM, ELECTRONIC DEVICE AND STORAGE MEDIUM 有权

    公开(公告)号：US20230029687A1

    公开(公告)日：2023-02-02

    申请号：US17655772

    申请日：2022-03-21

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

    Inventor： Jun Xu ,  Zeming Liu ,  Zeyang Lei ,  Zhengyu Niu ,  Hua Wu ,  Haifeng Wang

    IPC: G06F40/35 ,  G06F40/56 ,  G06N5/02

    Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.

    METHOD AND DEVICE FOR TRAINING SPEECH TRANSLATION MODEL, AND STORAGE MEDIUM
    8.
    发明申请
    METHOD AND DEVICE FOR TRAINING SPEECH TRANSLATION MODEL, AND STORAGE MEDIUM 有权

    公开(公告)号：US20250054494A1

    公开(公告)日：2025-02-13

    申请号：US18930081

    申请日：2024-10-29

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

    Inventor： Pengzhi Gao ,  Ruiqing Zhang ,  Zhongjun He ,  Hua Wu

    IPC: G10L15/06 ,  G10L15/02

    Abstract: A method for training a speech translation model includes: obtaining a trained first text translation model and a speech recognition model, and constructing a candidate speech translation model to be trained based on the first text translation model and the speech recognition model; obtaining at least one of a first sample source language speech or a first sample source language text to obtain a training sample of the candidate speech translation model; and training the candidate speech translation model based on the training sample until the training is completed, and obtaining a trained target speech translation model.

    METHOD AND APPARATUS FOR TRAINING SEMANTIC RETRIEVAL NETWORK, ELECTRONIC DEVICE AND STORAGE MEDIUM
    9.
    发明申请
    METHOD AND APPARATUS FOR TRAINING SEMANTIC RETRIEVAL NETWORK, ELECTRONIC DEVICE AND STORAGE MEDIUM 有权

    公开(公告)号：US20230004819A1

    公开(公告)日：2023-01-05

    申请号：US17930221

    申请日：2022-09-07

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

    Inventor： Yingqi Qu ,  Yuchen Ding ,  Jing Liu ,  Hua Wu ,  Haifeng Wang

    IPC: G06N5/00 ,  G06F40/30 ,  G06F16/2457

    Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

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