AI Student: A Machine Reading Comprehension System for the Korean College Scholastic Ability Test

Gyeongmin Kim, Soomin Lee, Chanjun Park and Jaechoon Jo
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Gyeongmin Kim: Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Soomin Lee: Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Chanjun Park: Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Jaechoon Jo: Division of Computer Engineering, Hanshin University, Osan 18101, Korea

Mathematics, 2022, vol. 10, issue 9, 1-12

Abstract: Machine reading comprehension is a question answering mechanism in which a machine reads, understands, and answers questions from a given text. These reasoning skills can be sufficiently grafted into the Korean College Scholastic Ability Test (CSAT) to bring about new scientific and educational advances. In this paper, we propose a novel Korean CSAT Question and Answering (KCQA) model and effectively utilize four easy data augmentation strategies with round trip translation to augment the insufficient data in the training dataset. To evaluate the effectiveness of KCQA, 30 students appeared for the test under conditions identical to the proposed model. Our qualitative and quantitative analysis along with experimental results revealed that KCQA achieved better performance than humans with a higher F1 score of 3.86.

Keywords: academic reading skills; Korean College Scholastic Ability Test; Korean CSAT question and answering; machine reading comprehension (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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