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1.发明申请公开(公告)号：US20230013796A1
公开(公告)日：2023-01-19
申请号：US17866104
申请日：2022-07-15
Inventor： Wenbin JIANG , Zhifan FENG , Xinwei FENG , Yajuan LYU , Yong ZHU
Abstract: The present disclosure provides a method and apparatus for acquiring a pre-trained model, an electronic device and a storage medium, and relates to the fields such as deep learning, natural language processing, knowledge graph and intelligent voice. The method may include: acquiring a pre-training task set composed of M pre-training tasks, M being a positive integer greater than 1, the pre-training tasks including: N question-answering tasks corresponding to different question-answering forms, N being a positive integer greater than 1 and less than or equal to M; and jointly pre-training the pre-trained model according to the M pre-training tasks.
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Patent Agency Ranking
公开(公告)号：US20230008897A1
公开(公告)日：2023-01-12
申请号：US17932598
申请日：2022-09-15
Inventor： Wenbin JIANG , Yajuan LYU , Yong ZHU , Hua WU , Haifeng WANG
IPC: G06F16/735
Abstract: An information search method includes: obtaining search words at least including a question to be searched and obtaining an initial text vector representation of the search words; obtaining a video corresponding to the search words, and obtaining multi-modality vector representations of the video; starting from the initial text vector representation, performing N rounds of interaction between the video and the search words based on the multi-modality vector representations and a text vector representation of the search words of a current round, to generate a target fusion vector representation, where N is an integer greater than or equal to 1; and obtaining target video frames matching the question to be searched by annotating the video based on the target fusion vector representation.
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3.发明公开公开(公告)号：US20230214688A1
公开(公告)日：2023-07-06
申请号：US18119494
申请日：2023-03-09
Inventor： Jiyuan ZHANG , Jianguo MAO , Zengfeng ZENG , Weihua PENG , Wenbin JIANG , Yajuan LYU
IPC: G06N5/04
CPC classification number: G06N5/04
Abstract: A method and apparatus for determining an answer to a question are provided. The method includes: splicing an acquired to-be-queried question with each candidate answer into each question-answer pair; performing reasoning operations of feature combination parameters on different granularity features of each question-answer pair at a preset number of steps in a horizontal direction based on recurrent characteristics of a recurrent neural network; determining feature combination weights of the different granularity features using multiple preset vertical reasoning layers at different reasoning focuses respectively, at each step of the reasoning operations in the horizontal direction; obtaining a candidate answer feature corresponding to each question-answer pair, respectively, through a final step of the reasoning operations; and determining a target candidate answer matching the to-be-queried question based on a feature similarity between a question feature of the to-be-queried question and each candidate answer feature.
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4.发明申请公开(公告)号：US20230092736A1
公开(公告)日：2023-03-23
申请号：US17872318
申请日：2022-07-25
Inventor： Wenbin JIANG , Yajuan LYU , Yong ZHU , Hua WU , Haifeng WANG
Abstract: The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.
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公开(公告)号：US20230018489A1
公开(公告)日：2023-01-19
申请号：US17862519
申请日：2022-07-12
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.
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公开(公告)号：US20210192142A1
公开(公告)日：2021-06-24
申请号：US17024756
申请日：2020-09-18
Inventor： Zhifan FENG , Haifeng WANG , Kexin REN , Yong ZHU , Yajuan LYU
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.
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