Method and apparatus of generating question-answer learning model through reinforcement learning
Assignee42 Maru Inc.
InventorsDong Hwan KIM, Woo Tae JEONG, Ki Bong SUNG
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Abstract
The present invention relates to a method of operating a question-answer model through reinforcement learning in an apparatus of generating an answer to a question. The method includes: sampling a latent variable from any passage by a first agent; extracting a question-answer dataset from the passage based on the latent variable; determining whether or not to apply the extracted question-answer dataset to learning of a question-answer model generating an answer to any question by a second agent; and applying a change value of performance of the question-answer model as a reward to the first agent and the second agent.
Claims11
A method of operating a question-answer model through reinforcement learning in an apparatus of generating an answer to a question, comprising:
- sampling a latent variable from any passage by a first agent;
- extracting a question-answer dataset from the passage based on the latent variable;
- determining whether or not to apply the extracted question-answer dataset to learning of a question-answer model generating an answer to any question by a second agent;
- and
- applying a change value of performance of the question-answer model as a reward to the first agent and the second agent.
A method of operating a question-answer learning model through adversarial learning in an apparatus of generating an answer to a question, comprising:
- sampling a latent variable based on constraints in any 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.
An apparatus of generating a question-answer dataset, comprising:
- one or more processors;
- and
- one or more memories in which instructions, when executed by the one or more processors, causing the one or more processors to perform an operation are stored,
- wherein the operation performed by the one or more processors includes:
- an operation of sampling a latent variable based on constraints in an input passage;
- an operation of generating an answer based on the latent variable;
- an operation of generating a question based on the answer;
- and
- an operation of machine-learning the question-answer learning model using a dataset of the generated question and answer,
- the constraints being controlled so that the latent variable is present in a data manifold while increasing a loss of the question-answer learning model.
Dependencies are inferred from the claim text.