[4Xin1] Poster session 2

[4Xin1-15] Improving Prediction Accuracy for Document Evaluation Problems Using Mixed-based Data Augmentation

〇Koki Inoue¹, Reoto Wakabayashi¹, Shoi Takahashi¹ (1.Elith Inc.)

Keywords:Data Augmentation, Natural language processing, Mixup, CutMix

Data augmentation techniques are an essential part of computer vision and can provide significant accuracy gains at a small engineering cost. Inspired by Mixup, one of the data enhancement techniques for blending images, we applied sentence-by-sentence Mixup to text. We show that this improves the accuracy of the task of predicting English learners' writing scores compared to methods that do not use mixup.