Hyesoo Kong  Hwamook Yoon  Mihwan Hyun  Hyejin Lee  Jaewook Seol

10.5626/JOK.2022.49.9.686

Machine Reading Comprehension question answering Language Model BERT

Recently, the performance of the Machine Reading Comprehension(MRC) system has been increased through various open-ended Question Answering(QA) task, and challenging QA task which has to comprehensively understand multiple text paragraphs and make discrete inferences is being released to train more intelligent MRC systems. However, due to the absence of a QA dataset for complex reasoning to understand academic information in Korean, MRC research on academic papers has been limited. In this paper, we constructed a QA dataset, KorSciQA 2.0, for the full text including abstracts of Korean academic papers and divided the difficulty level into general, easy, and hard for discriminative MRC systems. A methodology, process, and system for constructing KorSciQA 2.0 were proposed. We conducted MRC performance evaluation experiments and when fine-tuning based on the KorSciBERT model, which is a Korean-based BERT model for science and technology domains, the F1 score was 80.76%, showing the highest performance.