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公开(公告)号：US11928434B2
公开(公告)日：2024-03-12
申请号：US17444693
申请日：2021-08-09
Inventor： Jiachen Liu , Xinyan Xiao , Hua Wu , Haifeng Wang
IPC: G06F40/56 , G06F40/295 , G06N5/022 , G06N20/00
CPC classification number: G06F40/295 , G06N5/022 , G06N20/00 , G06F40/56
Abstract: A method for text generation, relates to a field of natural language processing, including: obtaining corpus data; labeling the corpus data to obtain a first constraint element; obtaining a first generation target; and generating a first text matching the first generation target by inputting the corpus data and the first constraint element into a generation model.
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公开(公告)号：US11704498B2
公开(公告)日：2023-07-18
申请号：US17200551
申请日：2021-03-12
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Zhongjun He , Zhi Li , Hua Wu
IPC: G06F40/30 , G06F40/51 , G06F40/44 , G06F40/49 , G06F18/214
CPC classification number: G06F40/30 , G06F18/214 , G06F40/44 , G06F40/49 , G06F40/51
Abstract: A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second training sample set.
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公开(公告)号：US11675983B2
公开(公告)日：2023-06-13
申请号：US17331526
申请日：2021-05-26
Inventor： Jiachen Liu , Zhe Hu , Xinyan Xiao , Hua Wu
IPC: G06F40/56 , G06N20/00 , G06F40/126 , G06F40/117
CPC classification number: G06F40/56 , G06F40/117 , G06F40/126 , G06N20/00
Abstract: A method for implementing text generation, a device and a medium are provided. The method includes: determining a target task type of a target text generation task from multiple task types supported by a pre-trained general text generation model; determining, based on a requirement of the target text generation task for a target output text, a first target output text attribute for the target text generation task from multiple output text attributes supported by the general text generation model; and fine tuning the general text generation model based on a target training data set associated with the target text generation task to obtain a task-specific text generation model, by taking task indication information for the target task type and first attribute indication information for the first target output text attribute as at least part of an input of the general text generation model.
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公开(公告)号：US11328133B2
公开(公告)日：2022-05-10
申请号：US16585269
申请日：2019-09-27
Inventor： Hao Xiong , Zhongjun He , Xiaoguang Hu , Hua Wu , Zhi Li , Zhou Xin , Tian Wu , Haifeng Wang
Abstract: The present disclosure provides a translation processing method, a translation processing device, and a device. The first speech signal of the first language is obtained, and the speech feature vector of the first speech signal is extracted based on the preset algorithm. Further, the speech feature vector is input into the pre-trained end-to-end translation model for conversion from the first language speech to the second language text for processing, and the text information of the second language corresponding to the first speech signal is obtained. Moreover, speech synthesis is performed on the text information of the second language, and the corresponding second speech signal is obtained and played.
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公开(公告)号：US20210390257A1
公开(公告)日：2021-12-16
申请号：US17116846
申请日：2020-12-09
Inventor： Chao Pang , Shuohuan Wang , Yu Sun , Hua Wu , Haifeng Wang
IPC: G06F40/295 , G06F40/30 , G06F40/137 , G06N5/02
Abstract: A method, an apparatus, a device and a storage medium for learning a knowledge representation are provided. The method can include: sampling a sub-graph of a knowledge graph from a knowledge base; serializing the sub-graph of the knowledge graph to obtain a serialized text; and reading using a pre-trained language model the serialized text in an order in the sub-graph of the knowledge graph, to perform learning to obtain a knowledge representation of each word in the serialized text. The knowledge representation learning in this embodiment is performed for entity and relationship representation learning in the knowledge base.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11995405B2
公开(公告)日：2024-05-28
申请号：US17348104
申请日：2021-06-15
Inventor： Xuan Ouyang , Shuohuan Wang , Chao Pang , Yu Sun , Hao Tian , Hua Wu , Haifeng Wang
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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公开(公告)号：US11928432B2
公开(公告)日：2024-03-12
申请号：US17319189
申请日：2021-05-13
Inventor： Fei Yu , Jiji Tang , Weichong Yin , Yu Sun , Hao Tian , Hua Wu , Haifeng Wang
CPC classification number: G06F40/284 , G06F40/30 , G06N5/04 , G06N20/00 , G06V10/811 , G06V20/30
Abstract: A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
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公开(公告)号：US11461549B2
公开(公告)日：2022-10-04
申请号：US16988907
申请日：2020-08-10
Inventor： Han Zhang , Dongling Xiao , Yukun Li , Yu Sun , Hao Tian , Hua Wu , Haifeng Wang
IPC: G06F40/274 , G06F40/56 , G06F40/30 , G06K9/62
Abstract: The present disclosure discloses a method and an apparatus for generating a text based on a semantic representation and relates to a field of natural language processing (NLP) technologies. The method for generating the text includes: obtaining an input text, the input text comprising a source text; obtaining a placeholder of an ith word to be predicted in a target text; obtaining a vector representation of the ith word to be predicted, in which the vector representation of the ith word to be predicted is obtained by calculating the placeholder of the ith word to be predicted, the source text and 1st to (i−1)th predicted words by employing a self-attention mechanism; and generating an ith predicted word based on the vector representation of the ith word to be predicted, to obtain a target text.
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公开(公告)号：US09910851B2
公开(公告)日：2018-03-06
申请号：US14893008
申请日：2014-11-12
Inventor： Haifeng Wang , Hua Wu
CPC classification number: G06F17/2854 , G06F17/2845 , G06F17/289 , G10L15/005 , G10L15/01
Abstract: Disclosed are on-line voice translation method and device. The method comprises: conducting voice recognition on first voice information input by a first user, so as to obtain first recognition information; prompting the first user to confirm the first recognition information; translating the confirmed first recognition information to obtain and output first translation information; extracting, according to second information which is fed back by a second user, associated information corresponding to the second information; and correcting the first translation information according to the associated information and outputting the corrected translation information. By means of the on-line voice translation method and device, smooth communication can be ensured in cross-language exchanges.
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公开(公告)号：US11995560B2
公开(公告)日：2024-05-28
申请号：US17043227
申请日：2020-04-07
Inventor： Quan Wang , Pingping Huang , Haifeng Wang , Wenbin Jiang , Yajuan Lyu , Yong Zhu , Hua Wu
IPC: G06N5/02 , G06F16/31 , G06F16/33 , G06F16/35 , G06F16/36 , G06F16/901 , G06F16/906 , G06F40/279 , G06N3/042 , G06N3/045 , G06N3/08 , G06N5/022
CPC classification number: G06N5/02 , G06N5/022 , G06F16/31 , G06F16/316 , G06F16/3347 , G06F16/35 , G06F16/36 , G06F16/367 , G06F16/9017 , G06F16/9024 , G06F16/906 , G06F40/279 , G06N3/042 , G06N3/045 , G06N3/08
Abstract: The present disclosure discloses a method and an apparatus for generating a vector representation of a knowledge graph, and relates to a field of a field of artificial intelligence technologies. The detailed implementing solution is: obtaining a knowledge graph, the knowledge graph including a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graph; and generating vector representations corresponding to the plurality of entity nodes by a context model based on the context data and the context type.
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