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公开(公告)号：US20240220812A1
公开(公告)日：2024-07-04
申请号：US17474950
申请日：2021-09-14
Inventor： Ruiqing ZHANG , Xiyang WANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06N3/094
CPC classification number: G06N3/094
Abstract: A method for training a machine translation (MT) model and an electronic device are provided. The technical solution includes: obtaining an original training sample configured to train an MT model; generating at least one adversarial training sample of the MT model based on the original training sample; training the MT model based on the original training sample and the adversarial training sample.
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公开(公告)号：US20230342560A1
公开(公告)日：2023-10-26
申请号：US18121351
申请日：2023-03-14
Inventor： Ruiqing ZHANG , Hui LIU , Xiyang WANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/47 , G06F40/166 , G06F40/30
CPC classification number: G06F40/47 , G06F40/166 , G06F40/30 , G06F40/247
Abstract: A text translation method is described that includes initially acquiring text. Thereafter, first text is determined in the initial text; and second text is determined according to the first text, where the second text is used for describing the first text. Additionally, initial text is translated to obtain initial translation text, and the second text is translated to obtain description translation text. Thereafter, the initial translation text is updated according to the description translation text to obtain target translation text of the initial text.
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公开(公告)号：US20220100786A1
公开(公告)日：2022-03-31
申请号：US17407320
申请日：2021-08-20
Inventor： Yuchen DING , Yingqi QU , Jing LIU , Kai LIU , Dou HONG , Hua WU , Haifeng WANG
Abstract: The present application discloses a method and apparatus for training a retrieval model, device and computer storage medium that relate to intelligent search and natural language processing technologies. An implementation includes: acquiring initial training data; performing a training operation using the initial training data to obtain an initial retrieval model; selecting texts with the correlation degrees with a query in the training data meeting a preset first requirement from candidate texts using the initial retrieval model; performing a training operation using the updated training data to obtain a first retrieval model; and selecting texts with the correlation degrees with the query in the training data meeting a preset second requirement from the candidate texts using the first retrieval model; and/or selecting texts with the correlation degrees with the query meeting a preset third requirement; and performing a training operation using the expanded training data to obtain a second retrieval model.
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公开(公告)号：US20220019744A1
公开(公告)日：2022-01-20
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
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：US20210326538A1
公开(公告)日：2021-10-21
申请号：US17362628
申请日：2021-06-29
Inventor： Chuanqiang ZHANG , Ruiqing ZHANG , Zhi LI , Zhongjun HE , Hua WU
Abstract: A method for text translation includes obtaining a text to be translated; and inputting the text to be translated into a text translation model. The trained text translation model divides the text to be translated into a plurality of semantic units, determines N semantic units before a current semantic unit among the plurality of semantic units as local context semantic units, determines M semantic units before the local context semantic units as global context semantic units, and generates a translation result of the current semantic unit based on the local context semantic units and the global context semantic units. N is an integer, and M is an integer.
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Patent Agency Ranking
公开(公告)号：US20210192150A1
公开(公告)日：2021-06-24
申请号：US16926197
申请日：2020-07-10
Inventor： Ruiqing ZHANG , Chuanqiang ZHANG , Hao XIONG , Zhongjun HE , Hua WU , Haifeng WANG
IPC: G06F40/55 , G06F40/58 , G06F40/211
Abstract: Embodiments of the present disclosure provide a language conversion method and apparatus based on syntactic linearity and a non-transitory computer-readable storage medium. The method includes: encoding a source sentence to be converted by using a preset encoder to determine a first vector and a second vector corresponding to the source sentence; determining a current mask vector according to a preset rule, in which the mask vector is configured to modify vectors output by the preset encoder; determining a third vector according to target language characters corresponding to source characters located before a first source character; and decoding the first vector, the second vector, the mask vector, and the third vector by using a preset decoder to generate a target character corresponding to the first source character.
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公开(公告)号：US20220180058A1
公开(公告)日：2022-06-09
申请号：US17383611
申请日：2021-07-23
Inventor： Ruiqing ZHANG , Chuanqiang ZHANG , Zhongjun HE , Zhi LI , Hua WU
IPC: G06F40/232 , G06F40/40 , G06F40/253
Abstract: The present disclosure provides a text error correction method, apparatus, electronic device and storage medium, and relates to the technical field of artificial intelligence such as natural language processing and deep learning. A specific implementation solution is: obtaining a current sentence and a historical sentence of the current sentence in an article to which the current sentence belongs; performing text error correction processing on the current sentence based on the current sentence and the historical sentence. According to the technical solutions of the present disclosure, text error correction can be performed on the current sentence based on the historical sentence, namely, the upper contextual information, of the current sentence in the article, so that the error correction information is richer and the error correction result is more accurate.
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公开(公告)号：US20210383797A1
公开(公告)日：2021-12-09
申请号：US17411917
申请日：2021-08-25
Inventor： Fan WANG , Siqi BAO , Huang HE , Hua WU , Jingzhou HE , Haifeng WANG
Abstract: A method for dialogue processing, an electronic device and a storage medium are provided. The specific technical solution includes: obtaining a dialogue history; selecting a target machine from a plurality of machines; inputting the dialogue history into a trained dialogue model in the target machine to generate a response to the dialogue history, in which the dialogue model comprises a common parameter and a specific parameter, and different machines correspond to the same common parameter.
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公开(公告)号：US20210383064A1
公开(公告)日：2021-12-09
申请号：US17101789
申请日：2020-11-23
Inventor： Shuohuan WANG , Siyu DING , Yu SUN , Hua WU , Haifeng WANG
IPC: G06F40/279 , G06F40/166 , G06F40/30 , G06N20/00
Abstract: The disclosure provides a text recognition method, an electronic device, and a storage medium. The method includes: obtaining N segments of a sample text; inputting each of the N segments into a preset initial language model in sequence, to obtain first text vector information corresponding to the N segments; inputting each of the N segments into the initial language model in sequence again, to obtain second text vector information corresponding to a currently input segment; in response to determining that the currently input segment has the mask, predicting the mask according to the second text vector information and the first text vector information to obtain a predicted word at a target position corresponding to the mask; training the initial language model according to an original word and the predicted word to generate a long text language model; and recognizing an input text through the long text language model.
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公开(公告)号：US20210319185A1
公开(公告)日：2021-10-14
申请号：US16953426
申请日：2020-11-20
Inventor： Jun XU , Zeyang LEI , Zhengyu NIU , Hua WU , Haifeng WANG
IPC: G06F40/30
Abstract: A method for generating a conversation, an electronic device and a storage medium, which relate to the field of artificial intelligence, are disclosed. The method may include: acquiring conversation content to be replied; determining an event node matched with the conversation content from an event graph, the event graph being a pre-constructed directed graph and including event nodes corresponding to different events respectively, and sides between the event nodes indicating logical relationships between the different events; determining an event node for guiding reply generation from the event graph according to the matched event node and the connection mode among the event nodes; and generating conversation reply content according to the event node for guiding reply generation. With the technical solution, dialog coherent, informative, and engaging multi-turn conversation may be generated.
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