TY  - JOUR
T1  - Data Augmentation Methods for Improving the Performance of Machine Reading Comprehension
AU  - Lee, Sunkyung 
AU  - Choi, Eunseong 
AU  - Jeong, Seonho 
AU  - Lee, Jongwuk 
JO  - Journal of KIISE, JOK
PY  - 2021
DA  - 2021/1/14
DO  - 10.5626/JOK.2021.48.12.1298
KW  - machine reading comprehension
KW  - data augmentation
KW  - question answering
KW  - language model
AB  - Machine reading comprehension is a method of understanding the meaning and performing inference over a given text by computers, and it is one of the most essential techniques for understanding natural language. The question answering task yields a way to test the reasoning ability of intelligent systems. Nowadays, machine reading comprehension techniques performance has significantly improved following the recent progress of deep neural networks. Nevertheless, there may be challenges in improving performance when data is sparse. To address this issue, we leverage word-level and sentence-level data augmentation techniques through text editing, while minimizing changes to the existing models and cost. In this work, we propose data augmentation methods for a pre-trained language model, which is most widely used in English question answering tasks, to confirm the improved performance over the existing models.