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公开(公告)号：EP3709207A1
公开(公告)日：2020-09-16
申请号：EP20150895.9
申请日：2020-01-09
发明人： HUANG, Jianhui , QIAO, Min , HUANG, Pingping , ZHU, Yong , LYU, Yajuan , LI, Ying
摘要： Embodiments of the present disclosure disclose a visual question answering model, an electronic device and a storage medium. The visual question answering model includes an image encoder and a text encoder. The text encoder is configured to perform pooling on a word vector sequence of a question text inputted, so as to extract a semantic representation vector of the question text; and the image encoder is configured to extract an image feature of a given image in combination with the semantic representation vector. By processing a text vector through pooling, the embodiments according to the present disclosure ensure that model training efficiency is effectively improved on the premise of a small loss of prediction accuracy of the visual question answering model, and thus the model is beneficial to the use in engineering.
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2.发明公开
公开(公告)号：EP4123474A1
公开(公告)日：2023-01-25
申请号：EP22183722.2
申请日：2022-07-08
发明人： JIANG, Wenbin , LYU, Yajuan , ZHU, Yong , WU, Hua , WANG, Haifeng
IPC分类号： G06F16/2452
摘要： 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. A QA effect can be improved through the technical solutions according to the present disclosure.
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3.发明公开
公开(公告)号：EP3913543A3
公开(公告)日：2022-04-27
申请号：EP21198333.3
申请日：2021-09-22
发明人： WANG, Quan , WANG, Haifeng , LYU, Yajuan , ZHU, Yong
摘要： A method and an apparatus for training a multivariate relationship generation model, an electronic device and a medium are provided. It relates to a field of computer technologies. The technical solution includes: obtaining (S101, S301) a plurality of knowledge text entries; performing (S102, S302) semantic parsing on each knowledge text entry to obtain a plurality of entities and semantic information of each knowledge text entry; constructing (S103, S303) a heterogeneous graph based on the plurality of entities and the semantic information; and training (S104) an initial artificial intelligence (AI) network model based on the heterogeneous graph to obtain a multivariate relationship generation model.
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4.发明公开
公开(公告)号：EP3913543A2
公开(公告)日：2021-11-24
申请号：EP21198333.3
申请日：2021-09-22
发明人： WANG, Quan , WANG, Haifeng , LYU, Yajuan , ZHU, Yong
摘要： A method and an apparatus for training a multivariate relationship generation model, an electronic device and a medium are provided. It relates to a field of computer technologies. The technical solution includes: obtaining (S101, S301) a plurality of knowledge text entries; performing (S102, S302) semantic parsing on each knowledge text entry to obtain a plurality of entities and semantic information of each knowledge text entry; constructing (S103, S303) a heterogeneous graph based on the plurality of entities and the semantic information; and training (S104) an initial artificial intelligence (AI) network model based on the heterogeneous graph to obtain a multivariate relationship generation model.
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5.发明公开
公开(公告)号：EP4152176A1
公开(公告)日：2023-03-22
申请号：EP22186185.9
申请日：2022-07-21
发明人： JIANG, Wenbin , LYU, Yajuan , ZHU, Yong , WU, Hua , WANG, Haifeng
IPC分类号： G06F16/332 , G06F16/33 , G06N3/04 , G06N3/06
摘要： 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. The technical solution according to the present disclosure may effectively improve an intelligent question-answering efficiency, and enhances intelligence.
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6.发明公开
公开(公告)号：EP4123516A1
公开(公告)日：2023-01-25
申请号：EP22184865.8
申请日：2022-07-14
发明人： JIANG, Wenbin , FENG, Zhifan , FENG, Xinwei , LYU, Yajuan , ZHU, Yong
IPC分类号： G06N3/08
摘要： 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. By use of the solutions of the present disclosure, resource consumption may be reduced, and time costs may be saved.
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公开(公告)号：EP4044045A1
公开(公告)日：2022-08-17
申请号：EP20767703.0
申请日：2020-04-07
发明人： WANG, Quan , HUANG, Pingping , WANG, Haifeng , JIANG, Wenbin , LYU, Yajuan , ZHU, Yong , WU, Hua
IPC分类号： G06F16/36
摘要： structure context model.
19. An electronic device, comprising:
at least one processor; and
a memory, communicatively coupled to the at least one processor,
wherein the memory is configured to store instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is caused to execute the method for generating the vector representation of the knowledge graph according to any one of claims 1-9.
20. A non-transitory computer readable storage medium having computer instructions stored thereon, wherein the computer instructions are configured to cause a computer to execute the method for generating the vector representation of the knowledge graph according to any one of claims 1-9.
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