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公开(公告)号:US20240143940A1
公开(公告)日:2024-05-02
申请号:US18544209
申请日:2023-12-18
Inventor: Dong Hwan KIM , Sung Ju HWANG , Seanie LEE , Dong Bok LEE , Woo Tae JEONG , Han Su KIM , You Kyung KWON , Hyun Ok KIM
Abstract: The present invention relates to a context-based QA generation architecture, and an object of the present invention is to generate diverse QA pairs from a single context. To achieve the object, the present invention includes a latent variable generating network including at least one encoder and an artificial neural network (Multi-Layer Perceptron: MLP) and configured to train the artificial neural network using a first context, a first question, and a first answer, and generate a second question latent variable and a second answer latent variable by applying the trained artificial neural network to a second context, an answer generating network configured to generate a second answer by decoding the second answer latent variable, and a question generating network configured to generate a second question based on a second context and the second answer.
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公开(公告)号:US20210166102A1
公开(公告)日:2021-06-03
申请号:US16699443
申请日:2019-11-29
Applicant: 42Maru Inc.
Inventor: Dong Hwan KIM , Woo Tae JEONG , Seanie LEE , Gilje SEONG
Abstract: A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.
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公开(公告)号:US20230342620A1
公开(公告)日:2023-10-26
申请号:US18214301
申请日:2023-06-26
Applicant: 42Maru Inc.
Inventor: Dong Hwan KIM , Woo Tae JEONG , Seanie LEE , Gilje SEONG
CPC classification number: G06N3/088 , G06N20/00 , G06N3/004 , G06F16/3334 , G06F16/3346 , G06N3/08 , G06F16/3347 , G06N3/045
Abstract: A method of generating a question-answer learning model through adversarial learning may include: sampling a latent variable based on constraints in an input passage; generating an answer based on the latent variable; generating a question based on the answer; and machine-learning the question-answer learning model using a dataset of the generated question and answer, wherein the constraints are controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.
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