QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Table 6: The F1 scores on the adversarial SQuAD test set.
['Single Model', 'AddSent', 'AddOneSent']
The model in use is exactly the one trained with the original SQuAD data (the one getting 84.6 F1 on test set), but now it is submitted to the adversarial server for evaluation. Again, we only compare the performance of single models. The robustness of our model is probably because it is trained with augmented data. The injected noise in the training data might not only improve the generalization of the model but also make it robust to the adversarial sentences.