  In this paper we present the results of an investigation of the importance of
verbs in a deep learning QA system trained on SQuAD dataset. We show that main
verbs in questions carry little influence on the decisions made by the system -
in over 90% of researched cases swapping verbs for their antonyms did not
change system decision. We track this phenomenon down to the insides of the
net, analyzing the mechanism of self-attention and values contained in hidden
layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset
as the source of the problem. Our work refers to the recently popular topic of
adversarial examples in NLP, combined with investigating deep net structure.
