公开(公告)号：US20230018489A1

公开(公告)日：2023-01-19

申请号：US17862519

申请日：2022-07-12

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

Inventor： Wenbin JIANG ,  Yajuan LYU ,  Yong ZHU ,  Hua WU ,  Haifeng WANG

IPC: G06F16/242 ,  G06F16/21 ,  G06F16/245 ,  G06N5/02

Abstract: The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

公开(公告)号：US20220293092A1

公开(公告)日：2022-09-15

申请号：US17828773

申请日：2022-05-31

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

Inventor： Siyu DING ,  Chao PANG ,  Shuohuan WANG ,  Yanbin ZHAO ,  Junyuan SHANG ,  Yu SUN ,  Shikun FENG ,  Hao TIAN ,  Hua WU ,  Haifeng WANG

IPC: G10L15/06 ,  G10L15/02 ,  G10L15/18

Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.

公开(公告)号：US20210209471A1

公开(公告)日：2021-07-08

申请号：US17211146

申请日：2021-03-24

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

Inventor： Haifeng WANG ,  Xiaoguang HU ,  Dianhai YU

IPC: G06N3/08 ,  G06N3/063 ,  G06F7/76

Abstract: The present application discloses a processor video memory optimization method and apparatus for deep learning training tasks, and relates to the technical field of artificial intelligence. In the method, by determining an optimal path for transferring a computing result, the computing result of a first computing unit is transferred to a second computing unit by using the optimal path. Thus, occupying the video memory is avoided, and meanwhile, a problem of low utilization rate of the computing unit of a GPU caused by video memory swaps is avoided, so that training speed of most tasks is hardly reduced.

公开(公告)号：US20210192364A1

公开(公告)日：2021-06-24

申请号：US17124030

申请日：2020-12-16

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

Inventor： Haifeng WANG ,  Wenbin JIANG ,  Yajuan LV ,  Yong ZHU ,  Hua WU

IPC: G06N5/02 ,  G06F40/30 ,  G06F40/279 ,  G06K9/62

Abstract: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

公开(公告)号：US20210192142A1

公开(公告)日：2021-06-24

申请号：US17024756

申请日：2020-09-18

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

Inventor： Zhifan FENG ,  Haifeng WANG ,  Kexin REN ,  Yong ZHU ,  Yajuan LYU

IPC: G06F40/30 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure discloses a multimodal content processing method, apparatus, device and storage medium, which relate to the technical field of artificial intelligence. The specific implementation is: receiving a content processing request of a user which is configured to request semantic understanding of multimodal content to be processed, analyzing the multimodal content to obtain the multimodal knowledge nodes corresponding to the multimodal content, determining a semantic understanding result of the multimodal content according to the multimodal knowledge nodes, a pre-constructed multimodal knowledge graph and the multimodal content, the multimodal knowledge graph including: the multimodal knowledge nodes and an association relationship between multimodal knowledge nodes. The technical solution can obtain an accurate semantic understanding result, realize an accurate application of multimodal content, and solve the problem in the prior art that multimodal content understanding is inaccurate.