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1.发明申请公开(公告)号：US20230004753A9
公开(公告)日：2023-01-05
申请号：US17209051
申请日：2021-03-22
Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.
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2.发明授权公开(公告)号：US11526668B2
公开(公告)日：2022-12-13
申请号：US17095955
申请日：2020-11-12
IPC: G06F40/279 , G06F16/9032 , G06N20/00 , G06F40/205 , G06K9/62
Abstract: A method and apparatus for obtaining word vectors based on a language model, a device and a storage medium are disclosed, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes inputting each of at least two first sample text language materials into the language model, and outputting a context vector of a first word mask in each first sample text language material via the language model; determining the word vector corresponding to each first word mask based on a first word vector parameter matrix, a second word vector parameter matrix and a fully connected matrix respectively; and training the language model and the fully connected matrix based on the word vectors corresponding to the first word masks in the at least two first sample text language materials, so as to obtain the word vectors.
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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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4.发明授权公开(公告)号：US12118063B2
公开(公告)日：2024-10-15
申请号：US17209051
申请日：2021-03-22
IPC: G06F40/30 , G06F18/214 , G06F18/2413
CPC classification number: G06F18/2148 , G06F18/24147 , G06F40/30
Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.
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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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Patent Agency Ranking
公开(公告)号：US11151177B2
公开(公告)日：2021-10-19
申请号：US16054842
申请日：2018-08-03
Inventor： Yukun Li , Yi Liu , Yu Sun , Dianhai Yu
Abstract: Embodiments of the present disclosure disclose a search method and apparatus based on artificial intelligence. A specific implementation of the method comprises: acquiring at least one candidate document related to a query sentence; determining a query word vector sequence corresponding to a segmented word sequence of the query sentence, and determining a candidate document word vector sequence corresponding to a segmented word sequence of each candidate document in the at least one candidate document; performing a similarity calculation for each candidate document in the at least one candidate document; selecting, in a descending order of similarities between the candidate document and the query sentence, a preset number of candidate documents from the at least one candidate document as a search result.
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公开(公告)号：US11562150B2
公开(公告)日：2023-01-24
申请号：US17031569
申请日：2020-09-24
Inventor： Han Zhang , Dongling Xiao , Yukun Li , Yu Sun , Hao Tian , Hua Wu , Haifeng Wang
Abstract: The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.
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公开(公告)号：US11556715B2
公开(公告)日：2023-01-17
申请号：US16951702
申请日：2020-11-18
IPC: G06F40/30 , G06N20/00 , G06F40/279
Abstract: A method for training a language model based on various word vectors, a device and a medium, which relate to the field of natural language processing technologies in artificial intelligence, are disclosed. An implementation includes inputting a first sample text language material including a first word mask into the language model, and outputting a context vector of the first word mask via the language model; acquiring a first probability distribution matrix of the first word mask based on the context vector of the first word mask and a first word vector parameter matrix, and a second probability distribution matrix of the first word mask based on the context vector of the first word mask and a second word vector parameter matrix; and training the language model based on a word vector corresponding to the first word mask.
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公开(公告)号：US11520991B2
公开(公告)日：2022-12-06
申请号：US16885358
申请日：2020-05-28
Inventor： Yu Sun , Haifeng Wang , Shuohuan Wang , Yukun Li , Shikun Feng , Hao Tian , Hua Wu
Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.
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10.发明申请公开(公告)号：US20220300763A1
公开(公告)日：2022-09-22
申请号：US17209051
申请日：2021-03-22
Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.
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