MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
Table 4: Performance (test accuracy (%)) on Yahoo! Answer with 10 labeled data and 5000 unlabeled data per class after removing different parts of MixText.
['[BOLD] Model', '[BOLD] Accuracy(%)']
We observed the performance drops after removing each part, suggesting that all components in MixText contribute to the final performance. The model performance decreased most significantly after removing unlabeled data which is as expected. Comparing to weighted average prediction for unlabeled data, the decrease from removing TMix was larger, indicating that TMix has the largest impact other than unlabeled data, which also proved the effectiveness of our proposed Text Mixup, an interpolation-based regularization and augmentation technique.