Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
Table 4: Adversarial accuracies on 200 random validation questions under the sentence-level black-box attacks (averaged results of nine models).
['Systems', 'Orig.', '[ITALIC] AddC', '[ITALIC] AddQ', '[ITALIC] AddQA']
The impact on performance is fairly small indicating the robustness of both models. While composing the sentence of just common English words (AddC) does not affect the models too much, adding words from the question and incorrect answers (AddQA) is most detrimental and causes both models to perform at or even below chance level. The models’ performance under AddQ, where the distractor sentence does not contain answer candidate words, is much higher than under AddQA. We observe that the models can be easily distracted by adding a single sequence of significant words, even though it bears no semantic relation to the rest of the plot. This suggests that both models heavily rely on the content of the provided answer candidates and might just perform matching of learned patterns to select the right answer.