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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: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:US11507748B2
公开(公告)日:2022-11-22
申请号:US16896465
申请日:2020-06-09
Inventor: Yuan Gao , Dai Dai , Xinyan Xiao
IPC: G06F40/295 , G06F40/242
Abstract: Embodiments of the present disclosure provide methods and apparatus for outputting information. The method may include: obtaining a sentence to be identified; Performing word segmentation on the to be identified sentence to obtain a word sequence; Inputting a word sequence into a pre-trained multi-task element recognition model based on sequence labeling and entity word prediction, and outputting the identified entity words, entity categories and entity word positions, where the multi-task element recognition model includes a sequence labeling network for performing sequence labeling tasks and an entity word predicting network for performing entity word predicting task, and the sequence labeling network is fused with the entity word predicting network through a fusion module.
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