42 Maru Inc.

Korea Advanced Institute of Science and Technology

Seanie Lee

Dong Hwan Kim

Woo Tae Jeong

Hyun Ok Kim

You Kyung Kwon

Han Su Kim

Sung Ju Hwang

Dong Bok Lee

Gilje Seong

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.

Method and apparatus for generating Q and A model by using adversarial learning

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.

Architecture for generating QA pairs from contexts

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Gyeonggi-do, South Korea

Seanie Lee