Learning Multi-level Dependencies for Robust Word Recognition
Table 5: Data augmentation results. The values that are higher than these of Table 4 are bold.
['[EMPTY]', '[EMPTY]', 'Test Noise Per', 'Test Noise W-PER', 'Test Noise DEL', 'Test Noise W-DEL', 'Test Noise INS', 'Test Noise W-INS', 'Test Noise SUB', 'Test Noise W-SUB', 'MEAN']
Thus, in the next, we apply the commonly used adversarial training method by augmenting all types of noise to train MUDE and test it on each type noise individually. As W- * (*∈ {PER, DEL, INS, SUB}) includes the *, in this experiment, we only combine W- * instead of all types of noise. We denote the new constructed training dataset as W-ALL. It can be observed from table that the MUDE trained on W-ALL has much better generalization ability (i.e., the mean value is much higher). In addition, it is interesting to see that performance of the MUDE decreases slightly in relatively easy cases where permutation or insertion noise presents while increasing a lot in difficult cases where deletion or substitution noise presents.