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公开(公告)号：US11954449B2
公开(公告)日：2024-04-09
申请号：US17475073
申请日：2021-09-14
Inventor： Fan Wang , Siqi Bao , Xinxian Huang , Hua Wu , Jingzhou He
IPC: G06F40/40 , G06F16/33 , G06F16/332 , G06N7/01 , G10L15/22
CPC classification number: G06F40/40 , G06F16/3329 , G06F16/3344 , G06N7/01 , G10L15/22
Abstract: The disclosure discloses a method for generating a conversation, an electronic device, and a storage medium. The detailed implementation includes: obtaining a current conversation and historical conversations of the current conversation; selecting multiple reference historical conversations from the historical conversations and adding the multiple reference historical conversations to a temporary conversation set; and generating reply information of the current conversation based on the current conversation and the temporary conversation set.
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22.发明授权公开(公告)号：US11914964B2
公开(公告)日：2024-02-27
申请号：US17209124
申请日：2021-03-22
Inventor： Shuohuan Wang , Jiaxiang Liu , Xuan Ouyang , Yu Sun , Hua Wu , Haifeng Wang
Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes: acquiring a semantic representation model which has been trained for a first language as a first semantic representation model; taking a bottom layer and a top layer of the first semantic representation model as trained layers, initializing the trained layers, keeping model parameters of other layers unchanged, and training the trained layers using training language materials of a second language until a training ending condition is met; successively bringing the untrained layers into the trained layers from bottom to top, and executing these layers respectively: keeping the model parameters of other layers than the trained layers unchanged, and training the trained layers using the training language materials of the second language until the training ending condition is met respectively; and obtaining a semantic representation model for the second language after all the layers are trained.
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公开(公告)号：US11574135B2
公开(公告)日：2023-02-07
申请号：US16861750
申请日：2020-04-29
Inventor： Haifeng Wang , Hua Wu , Zhongjun He , Hao Xiong
Abstract: The present disclosure provides a method, apparatus, electronic device and readable storage medium for translation and relates to translation technologies. In the embodiments of the present disclosure, the at least one knowledge element is obtained according to associated information of content to be translated, and respective knowledge element in the at least one knowledge element comprise an element of the first language type and an element of the second language type so that the at least one knowledge element can be used to obtain a translation result of the content to be translated. Since the at least one knowledge element obtained in advance is taken as global information of the translation task of this time, it can be ensured that the translation result of the same content to be translated is consistent, thereby improving the quality of the translation result.
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公开(公告)号：US11537798B2
公开(公告)日：2022-12-27
申请号：US16895297
申请日：2020-06-08
Inventor： Siqi Bao , Huang He , Junkun Chen , Fan Wang , Hua Wu , Jingzhou He
IPC: G06F40/30 , G06F16/332
Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a dialogue model. The method may include: acquiring a corpus sample set, a corpus sample including input information and target response information; classifying corpus samples in the corpus sample set, setting discrete hidden variables for the corpus samples based on a classification result to generate a training sample set, a training sample including the input information, the target response information, and a discrete hidden variable; and training a preset neural network using the training sample set to obtain the dialogue model, the dialogue model being used to represent a corresponding relationship between inputted input information and outputted target response information.
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公开(公告)号：US11423222B2
公开(公告)日：2022-08-23
申请号：US17243097
申请日：2021-04-28
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Zhongjun He , Zhi Li , Hua Wu
IPC: G06F40/232 , G06N20/00 , G06F40/279 , G06F40/166
Abstract: A method for text error correction includes: obtaining a text to be corrected; obtaining a pinyin sequence of the text to be corrected; and inputting the text to be corrected and the pinyin sequence to a text error correction model, to obtain a corrected text.
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Patent Agency Ranking
26.发明申请公开(公告)号：US20220004716A1
公开(公告)日：2022-01-06
申请号：US17209124
申请日：2021-03-22
Inventor： Shuohuan Wang , Jiaxiang Liu , Xuan Ouyang , Yu Sun , Hua Wu , Haifeng Wang
Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes: acquiring a semantic representation model which has been trained for a first language as a first semantic representation model; taking a bottom layer and a top layer of the first semantic representation model as trained layers, initializing the trained layers, keeping model parameters of other layers unchanged, and training the trained layers using training language materials of a second language until a training ending condition is met; successively bringing the untrained layers into the trained layers from bottom to top, and executing these layers respectively: keeping the model parameters of other layers than the trained layers unchanged, and training the trained layers using the training language materials of the second language until the training ending condition is met respectively; and obtaining a semantic representation model for the second language after all the layers are trained.
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27.发明授权公开(公告)号：US11182648B2
公开(公告)日：2021-11-23
申请号：US16901940
申请日：2020-06-15
Inventor： Hao Xiong , Zhongjun He , Zhi Li , Hua Wu , Haifeng Wang
IPC: G06K9/62 , G06F40/117 , G06N20/00
Abstract: The present disclosure provides an end-to-end model training method and apparatus, which relates to a field of artificial intelligence technologies. The method includes: obtaining training data containing a plurality of training samples, in which the plurality of training samples include an original sequence, a target sequence and a corresponding tag list, the tag list includes importance tags in the target sequence and avoidance tags corresponding to the importance tags, and the avoidance tags are irrelevant tags corresponding to the importance tags; and adopting the training data to train a preset end-to-end model until a value of a preset optimization target function is smaller than a preset threshold.
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公开(公告)号：US20210232765A1
公开(公告)日：2021-07-29
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 , G06F40/30 , G06F40/56 , 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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29.发明申请公开(公告)号：US20210200963A1
公开(公告)日：2021-07-01
申请号：US17200588
申请日：2021-03-12
Inventor： Ruiqing Zhang , Chuanqiang Zhang , Jiqiang Liu , Zhongjun He , Zhi Li , Hua Wu
Abstract: The present disclosure provides a machine translation model training method, apparatus, electronic device and storage medium, which relates to the technical field of natural language processing. A specific implementation solution is as follows: selecting, from parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which have universal-field features and/or target-field features, to constitute a first training sample set; selecting, from the parallel corpuses, a set of samples whose translation quality satisfies a preset requirement and which do not have universal-field features and target-field features, to constitute a second training sample set; training an encoder in the machine translation model in the target field, a discriminator configured in encoding layers of the encoder, and the encoder and a decoder in the machine translation model in the target field in turn with the first training sample set and second training sample set, respectively. The training method according to the present disclosure is time-saving and effort-saving, and may effectively improve the training efficiency of the machine translation model in the target field.
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公开(公告)号：US20210192151A1
公开(公告)日：2021-06-24
申请号：US16861750
申请日：2020-04-29
Inventor： Haifeng WANG , Hua Wu , Zhongjun He , Hao Xiong
Abstract: The present disclosure provides a method, apparatus, electronic device and readable storage medium for translation and relates to translation technologies. In the embodiments of the present disclosure, the at least one knowledge element is obtained according to associated information of content to be translated, and respective knowledge element in the at least one knowledge element comprise an element of the first language type and an element of the second language type so that the at least one knowledge element can be used to obtain a translation result of the content to be translated. Since the at least one knowledge element obtained in advance is taken as global information of the translation task of this time, it can be ensured that the translation result of the same content to be translated is consistent, thereby improving the quality of the translation result.
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