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公开(公告)号:US20240127710A1
公开(公告)日:2024-04-18
申请号:US18302637
申请日:2023-04-18
Inventor: Minsoo CHO , Oh Woog KWON , Yoon-Hyung ROH , Ki Young LEE , Yo Han LEE , Sung Kwon CHOI , Jinxia HUANG
IPC: G09B19/00 , G06F40/232 , G06F40/253 , G06F40/284
CPC classification number: G09B19/00 , G06F40/232 , G06F40/253 , G06F40/284
Abstract: Disclosed are a system and method for automatically evaluating an essay. The system includes a structure analysis module configured to divide learning data and learner essay text in a predetermined structure analysis unit, generate structure tagging information for each structure analysis unit, and structure the learning data and the learner essay text by attaching the structure tagging information to the learning data and the learner essay text, a learning module configured to generate an essay evaluation model through learning by using essay text that is included in the structured learning data and the structure tagging information as an input value and using an evaluation score that is included in the structured learning data as a label, and an evaluation module configured to generate essay evaluation results using the essay evaluation model.
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公开(公告)号:US20200250384A1
公开(公告)日:2020-08-06
申请号:US16751764
申请日:2020-01-24
Inventor: Yo Han LEE , Young Kil KIM
Abstract: Provided are a method and apparatus for constructing a compact translation model that may be installed on a terminal on the basis of a pre-built reference model, in which a pre-built reference model is miniaturized through a parameter imitation learning and is efficiently compressed through a tree search structure imitation learning without degrading the translation performance. The compact translation model provides translation accuracy and speed in a terminal environment that is limited in network, memory, and computation performance.
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公开(公告)号:US20210165976A1
公开(公告)日:2021-06-03
申请号:US17104381
申请日:2020-11-25
Inventor: Yo Han LEE , Young Kil KIM
IPC: G06F40/58 , G06F40/284 , G06N3/08
Abstract: Provided are a system and method for end-to-end neural machine translation. The method of end-to-end neural machine translation includes performing learning including a READ token on an end-to-end neural machine translation network, performing learning on an action network to learn a position of an actual segmentation point, and performing entire network re-learning on the end-to-end neural machine translation network and the action network.
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公开(公告)号:US20230134933A1
公开(公告)日:2023-05-04
申请号:US17964477
申请日:2022-10-12
Inventor: Yo Han LEE
IPC: G06N5/02 , G06N5/04 , G06F16/332
Abstract: Disclosed is a self-learning method of a knowledge-based dialogue system. The method includes generating, by the query generating module, a natural language query by itself based on a knowledge path for a query selected from a knowledge graph, inferring, by the answer generating module, a knowledge path for an answer corresponding to a query intention of the natural language query from the knowledge graph, and generating a natural language answer by itself based on the knowledge path for an answer, comparing and evaluating, by the dialogue evaluating unit, the knowledge path for a query and the knowledge path for an answer, and learning, by the query generating module and the answer generating module, a dialogue between the query generating module and the answer generating module based on an evaluation result of the dialogue evaluating unit.
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公开(公告)号:US20210165971A1
公开(公告)日:2021-06-03
申请号:US17104317
申请日:2020-11-25
Inventor: Yo Han LEE , Young Kil KIM
IPC: G06F40/47 , G06F40/284 , G06F40/30
Abstract: Provided are an apparatus and method for automatic translation, and more specifically, an apparatus and method for automatic translation between low-resource languages lacking learning data. The apparatus includes an inputter configured to receive a source language, which is a low-resource language, and a third language abundant in resources compared to the low-resource language, a memory configured to store a program for performing automatic translation between the source language, which is the low-resource language, and a target language using the third language, and a processor configured to execute the program, wherein the processor performs the automatic translation using a third language vocabulary embedding vector.
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